From fb3a8a5d899a8e71cd4872110c28f0d8783bdd4a Mon Sep 17 00:00:00 2001 From: cjhutto Date: Mon, 17 Nov 2014 11:32:00 -0500 Subject: [PATCH] Initial public (beta) release VADER Sentiment Analysis. VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media, and works well on texts from other domains. --- .gitignore | 56 + LICENSE.txt | 22 + MANIFEST.in | 3 + README.md | 215 + README.txt | 189 + additional_resources/hutto_ICWSM_2014.tar.gz | Bin 0 -> 2338514 bytes setup.cfg | 4 + setup.py | 22 + vaderSentiment/__init__.py | 1 + vaderSentiment/vaderSentiment.py | 363 + vaderSentiment/vader_sentiment_lexicon.txt | 7517 ++++++++++++++++++ 11 files changed, 8392 insertions(+) create mode 100644 .gitignore create mode 100644 LICENSE.txt create mode 100644 MANIFEST.in create mode 100644 README.md create mode 100644 README.txt create mode 100644 additional_resources/hutto_ICWSM_2014.tar.gz create mode 100644 setup.cfg create mode 100644 setup.py create mode 100644 vaderSentiment/__init__.py create mode 100644 vaderSentiment/vaderSentiment.py create mode 100644 vaderSentiment/vader_sentiment_lexicon.txt diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..7eeaf70 --- /dev/null +++ b/.gitignore @@ -0,0 +1,56 @@ +# Windows image file caches +Thumbs.db +ehthumbs.db + +# Folder config file +Desktop.ini + +# Recycle Bin used on file shares +$RECYCLE.BIN/ + +# Windows Installer files +*.cab +*.msi +*.msm +*.msp + +# ========================= +# Operating System Files +# ========================= + +# OSX +# ========================= + +.DS_Store +.AppleDouble +.LSOverride + +# Icon must end with two \r +Icon + +# Thumbnails +._* + +# Files that might appear on external disk +.Spotlight-V100 +.Trashes + +# Directories potentially created on remote AFP share +.AppleDB +.AppleDesktop +Network Trash Folder +Temporary Items +.apdisk + +# Compiled python modules. +*.pyc + +# Setuptools distribution folder. +/dist/ + +# Python egg metadata, regenerated from source files by setuptools. +/*.egg-info + +# personal notes and help +*.notes +*.help \ No newline at end of file diff --git a/LICENSE.txt b/LICENSE.txt new file mode 100644 index 0000000..07b8e86 --- /dev/null +++ b/LICENSE.txt @@ -0,0 +1,22 @@ +The MIT License (MIT) + +Copyright (c) 2014 cjhutto + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. + diff --git a/MANIFEST.in b/MANIFEST.in new file mode 100644 index 0000000..398aa9a --- /dev/null +++ b/MANIFEST.in @@ -0,0 +1,3 @@ +include *.txt *.py *.md +recursive-include vaderSentiment *.txt *.py +recursive-include additional_resources *.tar.gz \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..b3da90e --- /dev/null +++ b/README.md @@ -0,0 +1,215 @@ +## VADER-Sentiment-Analysis +======================== + +VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is _specifically attuned to sentiments expressed in social media_. It is fully open-sourced under the [MIT License](http://choosealicense.com/) (we sincerely appreciate all attributions and readily accept most contributions, but please don't hold us liable). + +======= + +###Introduction + +This README file describes the dataset of the paper: + + **VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text**
+ (by C.J. Hutto and Eric Gilbert)
+ Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.
+ +For questions, please contact:
+ +C.J. Hutto
+Georgia Institute of Technology, Atlanta, GA 30032
+cjhutto [at] gatech [dot] edu
+ +======= + +###Citation Information + +If you use either the dataset or any of the VADER sentiment analysis tools (VADER sentiment lexicon or Python code for rule-based sentiment analysis engine) in your research, please cite the above paper. For example:
+ + > **Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.**
+ +======= + +###Installation + +There are a couple of ways to install and use VADER sentiment:
+- The simplest is to use the command line to do an installtion from PyPI using pip, e.g., +``` +> pip install vaderSentiment +``` +- You could also clone the [GitHub repository](https://github.com/cjhutto/vaderSentiment) (HTTPS clone URL: https://github.com/cjhutto/vaderSentiment.git) +- You could simply download either the [full master branch zip file](https://github.com/cjhutto/vaderSentiment/archive/master.zip) , or the [public release source code](https://github.com/cjhutto/vaderSentiment/releases/tag/0.5) as a [compressed .zip file](https://github.com/cjhutto/vaderSentiment/archive/0.5.zip) or [tarball .tar.gz file](https://github.com/cjhutto/vaderSentiment/archive/0.5.tar.gz) + +======= + +###Resources and Dataset Descriptions + +The compressed .tar.gz package includes **PRIMARY RESOURCES** (items 1-3) as well as additional **DATASETS AND TESTING RESOURCES** (items 4-12): + +1. vader_icwsm2014_final.pdf
+ The original paper for the data set, see citation information (above). + +2. vader_sentiment_lexicon.txt
+ Empirically validated by multiple independent human judges, VADER incorporates a "gold-standard" sentiment lexicon that is especially attuned to microblog-like contexts.
+ The VADER sentiment lexicon is sensitive both the **polarity** and the **intensity** of sentiments + expressed in social media contexts, and is also generally applicable to sentiment analysis + in other domains.
+ Manually creating (much less, validating) a comprehensive sentiment lexicon is + a labor intensive and sometimes error prone process, so it is no wonder that many + opinion mining researchers and practitioners rely so heavily on existing lexicons + as primary resources. We are pleased to offer ours as a new resource.
+ We begin by constructing a list inspired by examining existing well-established + sentiment word-banks (LIWC, ANEW, and GI). To this, we next incorporate numerous + lexical features common to sentiment expression in microblogs, including + - a full list of Western-style emoticons, for example, :-) denotes a smiley face + and generally indicates positive sentiment) + - sentiment-related acronyms and initialisms (e.g., LOL and WTF are both examples of + sentiment-laden initialisms) + - commonly used slang with sentiment value (e.g., nah, meh and giggly). + + This process provided us with over 9,000 lexical feature candidates. Next, we assessed + the general applicability of each feature candidate to sentiment expressions. We + used a wisdom-of-the-crowd13 (WotC) approach (Surowiecki, 2004) to acquire a valid + point estimate for the sentiment valence (intensity) of each context-free candidate + feature. We collected intensity ratings on each of our candidate lexical features + from ten independent human raters (for a total of 90,000+ ratings). Features were + rated on a scale from "[–4] Extremely Negative" to "[4] Extremely Positive", with + allowance for "[0] Neutral (or Neither, N/A)".
+ We kept every lexical feature that had a non-zero mean rating, and whose standard + deviation was less than 2.5 as determined by the aggregate of ten independent raters. + This left us with just over 7,500 lexical features with validated valence scores that + indicated both the sentiment polarity (positive/negative), and the sentiment intensity + on a scale from –4 to +4. For example, the word "okay" has a positive valence of 0.9, + "good" is 1.9, and "great" is 3.1, whereas "horrible" is –2.5, the frowning emoticon :( + is –2.2, and "sucks" and it's slang derivative "sux" are both –1.5. + +3. vaderSentiment.py
+ The Python code for the rule-based sentiment analysis engine. Implements the + grammatical and syntactical rules described in the paper, incorporating empirically + derived quantifications for the impact of each rule on the perceived intensity of + sentiment in sentence-level text. Importantly, these heuristics go beyond what would + normally be captured in a typical bag-of-words model. They incorporate **word-order + sensitive relationships** between terms. For example, degree modifiers (also called + intensifiers, booster words, or degree adverbs) impact sentiment intensity by either + increasing or decreasing the intensity. Consider these examples:
+ (a) "The service here is extremely good"
+ (b) "The service here is good"
+ (c) "The service here is marginally good"
+ From Table 3 in the paper, we see that for 95% of the data, using a degree modifier + increases the positive sentiment intensity of example (a) by 0.227 to 0.36, with a + mean difference of 0.293 on a rating scale from 1 to 4. Likewise, example (c) reduces + the perceived sentiment intensity by 0.293, on average. + +4. tweets_GroundTruth.txt
+ FORMAT: the file is tab delimited with ID, MEAN-SENTIMENT-RATING, and TWEET-TEXT
+ DESCRIPTION: includes "tweet-like" text as inspired by 4,000 tweets pulled from Twitter’s public timeline, plus 200 completely contrived tweet-like texts intended to specifically test syntactical and grammatical conventions of conveying differences in sentiment intensity. The "tweet-like" texts incorporate a fictitious username (@anonymous) in places where a username might typically appear, along with a fake URL ( http://url_removed ) in places where a URL might typically appear, as inspired by the original tweets. The ID and MEAN-SENTIMENT-RATING correspond to the raw sentiment rating data provided in 'tweets_anonDataRatings.txt' (described below). + +5. tweets_anonDataRatings.txt
+ FORMAT: the file is tab delimited with ID, MEAN-SENTIMENT-RATING, STANDARD DEVIATION, and RAW-SENTIMENT-RATINGS
+ DESCRIPTION: Sentiment ratings from a minimum of 20 independent human raters (all pre-screened, trained, and quality checked for optimal inter-rater reliability). + +6. nytEditorialSnippets_GroundTruth.txt
+ FORMAT: the file is tab delimited with ID, MEAN-SENTIMENT-RATING, and TEXT-SNIPPET
+ DESCRIPTION: includes 5,190 sentence-level snippets from 500 New York Times opinion news editorials/articles; we used the NLTK tokenizer to segment the articles into sentence phrases, and added sentiment intensity ratings. The ID and MEAN-SENTIMENT-RATING correspond to the raw sentiment rating data provided in 'nytEditorialSnippets_anonDataRatings.txt' (described below). + +7. nytEditorialSnippets_anonDataRatings.txt
+ FORMAT: the file is tab delimited with ID, MEAN-SENTIMENT-RATING, STANDARD DEVIATION, and RAW-SENTIMENT-RATINGS
+ DESCRIPTION: Sentiment ratings from a minimum of 20 independent human raters (all pre-screened, trained, and quality checked for optimal inter-rater reliability). + +8. movieReviewSnippets_GroundTruth.txt
+ FORMAT: the file is tab delimited with ID, MEAN-SENTIMENT-RATING, and TEXT-SNIPPET
+ DESCRIPTION: includes 10,605 sentence-level snippets from rotten.tomatoes.com. The snippets were derived from an original set of 2000 movie reviews (1000 positive and 1000 negative) in Pang & Lee (2004); we used the NLTK tokenizer to segment the reviews into sentence phrases, and added sentiment intensity ratings. The ID and MEAN-SENTIMENT-RATING correspond to the raw sentiment rating data provided in 'movieReviewSnippets_anonDataRatings.txt' (described below). + +9. movieReviewSnippets_anonDataRatings.txt
+ FORMAT: the file is tab delimited with ID, MEAN-SENTIMENT-RATING, STANDARD DEVIATION, and RAW-SENTIMENT-RATINGS
+ DESCRIPTION: Sentiment ratings from a minimum of 20 independent human raters (all pre-screened, trained, and quality checked for optimal inter-rater reliability). + +10. amazonReviewSnippets_GroundTruth.txt
+ FORMAT: the file is tab delimited with ID, MEAN-SENTIMENT-RATING, and TEXT-SNIPPET
+ DESCRIPTION: includes 3,708 sentence-level snippets from 309 customer reviews on 5 different products. The reviews were originally used in Hu & Liu (2004); we added sentiment intensity ratings. The ID and MEAN-SENTIMENT-RATING correspond to the raw sentiment rating data provided in 'amazonReviewSnippets_anonDataRatings.txt' (described below). + +11. amazonReviewSnippets_anonDataRatings.txt
+ FORMAT: the file is tab delimited with ID, MEAN-SENTIMENT-RATING, STANDARD DEVIATION, and RAW-SENTIMENT-RATINGS
+ DESCRIPTION: Sentiment ratings from a minimum of 20 independent human raters (all pre-screened, trained, and quality checked for optimal inter-rater reliability). + +
+12. Comp.Social website with more papers/research:
+ [Comp.Social](http://comp.social.gatech.edu/papers/) +13. vader_sentiment_comparison_online_weblink
+ A short-cut hyperlinked to the online (web-based) sentiment comparison using a "light" version of VADER. http://www.socialai.gatech.edu/apps/sentiment.html . + + +======= +##Python Code EXAMPLE: +``` + from vaderSentiment import sentiment as vaderSentiment + #note: depending on how you installed (e.g., using source code download versus pip install), you may need to import like this: + #from vaderSentiment.vaderSentiment import sentiment as vaderSentiment + + # --- example sentences ------- + sentences = [ + "VADER is smart, handsome, and funny.", # positive sentence example + "VADER is smart, handsome, and funny!", # punctuation emphasis handled correctly (sentiment intensity adjusted) + "VADER is very smart, handsome, and funny.", # booster words handled correctly (sentiment intensity adjusted) + "VADER is VERY SMART, handsome, and FUNNY.", # emphasis for ALLCAPS handled + "VADER is VERY SMART, handsome, and FUNNY!!!",# combination of signals - VADER appropriately adjusts intensity + "VADER is VERY SMART, really handsome, and INCREDIBLY FUNNY!!!",# booster words & punctuation make this close to ceiling for score + "The book was good.", # positive sentence + "The book was kind of good.", # qualified positive sentence is handled correctly (intensity adjusted) + "The plot was good, but the characters are uncompelling and the dialog is not great.", # mixed negation sentence + "A really bad, horrible book.", # negative sentence with booster words + "At least it isn't a horrible book.", # negated negative sentence with contraction + ":) and :D", # emoticons handled + "", # an empty string is correctly handled + "Today sux", # negative slang handled + "Today sux!", # negative slang with punctuation emphasis handled + "Today SUX!", # negative slang with capitalization emphasis + "Today kinda sux! But I'll get by, lol" # mixed sentiment example with slang and constrastive conjunction "but" + ]. + for sentence in sentences: + print sentence, + vs = vaderSentiment(sentence) + print "\n\t" + str(vs) + +# --- output for the above example code --- +VADER is smart, handsome, and funny. + {'neg': 0.0, 'neu': 0.254, 'pos': 0.746, 'compound': 0.8316} +VADER is smart, handsome, and funny! + {'neg': 0.0, 'neu': 0.248, 'pos': 0.752, 'compound': 0.8439} +VADER is very smart, handsome, and funny. + {'neg': 0.0, 'neu': 0.299, 'pos': 0.701, 'compound': 0.8545} +VADER is VERY SMART, handsome, and FUNNY. + {'neg': 0.0, 'neu': 0.246, 'pos': 0.754, 'compound': 0.9227} +VADER is VERY SMART, handsome, and FUNNY!!! + {'neg': 0.0, 'neu': 0.233, 'pos': 0.767, 'compound': 0.9342} +VADER is VERY SMART, really handsome, and INCREDIBLY FUNNY!!! + {'neg': 0.0, 'neu': 0.294, 'pos': 0.706, 'compound': 0.9469} +The book was good. + {'neg': 0.0, 'neu': 0.508, 'pos': 0.492, 'compound': 0.4404} +The book was kind of good. + {'neg': 0.0, 'neu': 0.657, 'pos': 0.343, 'compound': 0.3832} +The plot was good, but the characters are uncompelling and the dialog is not great. + {'neg': 0.327, 'neu': 0.579, 'pos': 0.094, 'compound': -0.7042} +A really bad, horrible book. + {'neg': 0.791, 'neu': 0.209, 'pos': 0.0, 'compound': -0.8211} +At least it isn't a horrible book. + {'neg': 0.0, 'neu': 0.637, 'pos': 0.363, 'compound': 0.431} +:) and :D + {'neg': 0.0, 'neu': 0.124, 'pos': 0.876, 'compound': 0.7925} + + {'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0} +Today sux + {'neg': 0.714, 'neu': 0.286, 'pos': 0.0, 'compound': -0.3612} +Today sux! + {'neg': 0.736, 'neu': 0.264, 'pos': 0.0, 'compound': -0.4199} +Today SUX! + {'neg': 0.779, 'neu': 0.221, 'pos': 0.0, 'compound': -0.5461} +Today kinda sux! But I'll get by, lol + {'neg': 0.195, 'neu': 0.531, 'pos': 0.274, 'compound': 0.2228} +``` +======= + +###Online (web-based) Sentiment Comparison using VADER + + http://www.socialai.gatech.edu/apps/sentiment.html . + +======= diff --git a/README.txt b/README.txt new file mode 100644 index 0000000..e936ae9 --- /dev/null +++ b/README.txt @@ -0,0 +1,189 @@ +======= + +Introduction + +This README file describes the dataset of the paper: + VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text + C.J. Hutto and Eric Gilbert + Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014. + +For questions, please contact: +C.J. Hutto +Georgia Institute of Technology, Atlanta, GA 30032 +cjhutto@gatech.edu + +======= + +Citation Information + +If you use either the dataset or any of the VADER sentiment analysis tools (VADER sentiment lexicon or Python code for rule-based sentiment analysis engine) in your research, please cite the above paper. For example: + + Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014. + +======= + +Resource and Dataset Descriptions + +This zip file includes: + +PRIMARY RESOURCES: +1. vader_icwsm2014_final.pdf + The original paper for the data set, see citation information (above). +2. vader_sentiment_lexicon.txt + Empirically validated by multiple independent human judges, VADER incorporates a + "gold-standard" sentiment lexicon that is especially attuned to microblog-like contexts. + The VADER sentiment lexicon is sensitive both the polarity and the intensity of sentiments + expressed in social media contexts, and is also generally applicable to sentiment analysis + in other domains. + Manually creating (much less, validating) a comprehensive sentiment lexicon is + a labor intensive and sometimes error prone process, so it is no wonder that many + opinion mining researchers and practitioners rely so heavily on existing lexicons + as primary resources. We are pleased to offer ours as a new resource. + We begin by constructing a list inspired by examining existing well-established + sentiment word-banks (LIWC, ANEW, and GI). To this, we next incorporate numerous + lexical features common to sentiment expression in microblogs, including + - a full list of Western-style emoticons, for example, :-) denotes a smiley face + and generally indicates positive sentiment) + - sentiment-related acronyms and initialisms (e.g., LOL and WTF are both examples of + sentiment-laden initialisms) + - commonly used slang with sentiment value (e.g., nah, meh and giggly). + This process provided us with over 9,000 lexical feature candidates. Next, we assessed + the general applicability of each feature candidate to sentiment expressions. We + used a wisdom-of-the-crowd13 (WotC) approach (Surowiecki, 2004) to acquire a valid + point estimate for the sentiment valence (intensity) of each context-free candidate + feature. We collected intensity ratings on each of our candidate lexical features + from ten independent human raters (for a total of 90,000+ ratings). Features were + rated on a scale from "[–4] Extremely Negative" to "[4] Extremely Positive", with + allowance for "[0] Neutral (or Neither, N/A)". + We kept every lexical feature that had a non-zero mean rating, and whose standard + deviation was less than 2.5 as determined by the aggregate of ten independent raters. + This left us with just over 7,500 lexical features with validated valence scores that + indicated both the sentiment polarity (positive/negative), and the sentiment intensity + on a scale from –4 to +4. For example, the word "okay" has a positive valence of 0.9, + "good" is 1.9, and "great" is 3.1, whereas "horrible" is –2.5, the frowning emoticon :( + is –2.2, and "sucks" and it's slang derivative "sux" are both –1.5. +3. vaderSentiment.py + The Python code for the rule-based sentiment analysis engine. Implements the + grammatical and syntactical rules described in the paper, incorporating empirically + derived quantifications for the impact of each rule on the perceived intensity of + sentiment in sentence-level text. Importantly, these heuristics go beyond what would + normally be captured in a typical bag-of-words model. They incorporate **word-order + sensitive relationships** between terms. For example, degree modifiers (also called + intensifiers, booster words, or degree adverbs) impact sentiment intensity by either + increasing or decreasing the intensity. Consider these examples: + (a) "The service here is extremely good" + (b) "The service here is good" + (c) "The service here is marginally good" + From Table 3 in the paper, we see that for 95% of the data, using a degree modifier + increases the positive sentiment intensity of example (a) by 0.227 to 0.36, with a + mean difference of 0.293 on a rating scale from 1 to 4. Likewise, example (c) reduces + the perceived sentiment intensity by 0.293, on average. + +DATASETS AND TESTING RESOURCES: +4. tweets_GroundTruth.txt + FORMAT: the file is tab delimited with ID, MEAN-SENTIMENT-RATING, and TWEET-TEXT + DESCRIPTION: includes "tweet-like" text as inspired by 4,000 tweets pulled from Twitter’s public timeline, plus 200 completely contrived tweet-like texts intended to specifically test syntactical and grammatical conventions of conveying differences in sentiment intensity. The "tweet-like" texts incorporate a fictitious username (@anonymous) in places where a username might typically appear, along with a fake URL ( http://url_removed ) in places where a URL might typically appear, as inspired by the original tweets. The ID and MEAN-SENTIMENT-RATING correspond to the raw sentiment rating data provided in 'tweets_anonDataRatings.txt' (described below). +5. tweets_anonDataRatings.txt + FORMAT: the file is tab delimited with ID, MEAN-SENTIMENT-RATING, STANDARD DEVIATION, and RAW-SENTIMENT-RATINGS + DESCRIPTION: Sentiment ratings from a minimum of 20 independent human raters (all pre-screened, trained, and quality checked for optimal inter-rater reliability). +6. nytEditorialSnippets_GroundTruth.txt + FORMAT: the file is tab delimited with ID, MEAN-SENTIMENT-RATING, and TEXT-SNIPPET + DESCRIPTION: includes 5,190 sentence-level snippets from 500 New York Times opinion news editorials/articles; we used the NLTK tokenizer to segment the articles into sentence phrases, and added sentiment intensity ratings. The ID and MEAN-SENTIMENT-RATING correspond to the raw sentiment rating data provided in 'nytEditorialSnippets_anonDataRatings.txt' (described below). +7. nytEditorialSnippets_anonDataRatings.txt + FORMAT: the file is tab delimited with ID, MEAN-SENTIMENT-RATING, STANDARD DEVIATION, and RAW-SENTIMENT-RATINGS + DESCRIPTION: Sentiment ratings from a minimum of 20 independent human raters (all pre-screened, trained, and quality checked for optimal inter-rater reliability). +8. movieReviewSnippets_GroundTruth.txt + FORMAT: the file is tab delimited with ID, MEAN-SENTIMENT-RATING, and TEXT-SNIPPET + DESCRIPTION: includes 10,605 sentence-level snippets from rotten.tomatoes.com. The snippets were derived from an original set of 2000 movie reviews (1000 positive and 1000 negative) in Pang & Lee (2004); we used the NLTK tokenizer to segment the reviews into sentence phrases, and added sentiment intensity ratings. The ID and MEAN-SENTIMENT-RATING correspond to the raw sentiment rating data provided in 'movieReviewSnippets_anonDataRatings.txt' (described below). +9. movieReviewSnippets_anonDataRatings.txt + FORMAT: the file is tab delimited with ID, MEAN-SENTIMENT-RATING, STANDARD DEVIATION, and RAW-SENTIMENT-RATINGS + DESCRIPTION: Sentiment ratings from a minimum of 20 independent human raters (all pre-screened, trained, and quality checked for optimal inter-rater reliability). +10. amazonReviewSnippets_GroundTruth.txt + FORMAT: the file is tab delimited with ID, MEAN-SENTIMENT-RATING, and TEXT-SNIPPET + DESCRIPTION: includes 3,708 sentence-level snippets from 309 customer reviews on 5 different products. The reviews were originally used in Hu & Liu (2004); we added sentiment intensity ratings. The ID and MEAN-SENTIMENT-RATING correspond to the raw sentiment rating data provided in 'amazonReviewSnippets_anonDataRatings.txt' (described below). +11. amazonReviewSnippets_anonDataRatings.txt + FORMAT: the file is tab delimited with ID, MEAN-SENTIMENT-RATING, STANDARD DEVIATION, and RAW-SENTIMENT-RATINGS + DESCRIPTION: Sentiment ratings from a minimum of 20 independent human raters (all pre-screened, trained, and quality checked for optimal inter-rater reliability). + +ADDITIONAL RESOURCES: +12. _README_.txt + This readme file +13. vader_sentiment_comparison_online_weblink + A short-cut hyperlinked to the online (web-based) sentiment comparison using a "light" version of VADER. http://www.socialai.gatech.edu/apps/sentiment.html . + +======= + +Python Code EXAMPLE: + + from vaderSentiment import sentiment as vaderSentiment + #note: depending on how you installed (e.g., using source code download versus pip install), you may need to import like this: + #from vaderSentiment.vaderSentiment import sentiment as vaderSentiment + + # --- example sentences ------- + sentences = [ + "VADER is smart, handsome, and funny.", # positive sentence example + "VADER is smart, handsome, and funny!", # punctuation emphasis handled correctly (sentiment intensity adjusted) + "VADER is very smart, handsome, and funny.", # booster words handled correctly (sentiment intensity adjusted) + "VADER is VERY SMART, handsome, and FUNNY.", # emphasis for ALLCAPS handled + "VADER is VERY SMART, handsome, and FUNNY!!!",# combination of signals - VADER appropriately adjusts intensity + "VADER is VERY SMART, really handsome, and INCREDIBLY FUNNY!!!",# booster words & punctuation make this close to ceiling for score + "The book was good.", # positive sentence + "The book was kind of good.", # qualified positive sentence is handled correctly (intensity adjusted) + "The plot was good, but the characters are uncompelling and the dialog is not great.", # mixed negation sentence + "A really bad, horrible book.", # negative sentence with booster words + "At least it isn't a horrible book.", # negated negative sentence with contraction + ":) and :D", # emoticons handled + "", # an empty string is correctly handled + "Today sux", # negative slang handled + "Today sux!", # negative slang with punctuation emphasis handled + "Today SUX!", # negative slang with capitalization emphasis + "Today kinda sux! But I'll get by, lol" # mixed sentiment example with slang and constrastive conjunction "but" + ]. + for sentence in sentences: + print sentence, + vs = vaderSentiment(sentence) + print "\n\t" + str(vs) + +# --- output for the above example code --- +VADER is smart, handsome, and funny. + {'neg': 0.0, 'neu': 0.254, 'pos': 0.746, 'compound': 0.8316} +VADER is smart, handsome, and funny! + {'neg': 0.0, 'neu': 0.248, 'pos': 0.752, 'compound': 0.8439} +VADER is very smart, handsome, and funny. + {'neg': 0.0, 'neu': 0.299, 'pos': 0.701, 'compound': 0.8545} +VADER is VERY SMART, handsome, and FUNNY. + {'neg': 0.0, 'neu': 0.246, 'pos': 0.754, 'compound': 0.9227} +VADER is VERY SMART, handsome, and FUNNY!!! + {'neg': 0.0, 'neu': 0.233, 'pos': 0.767, 'compound': 0.9342} +VADER is VERY SMART, really handsome, and INCREDIBLY FUNNY!!! + {'neg': 0.0, 'neu': 0.294, 'pos': 0.706, 'compound': 0.9469} +The book was good. + {'neg': 0.0, 'neu': 0.508, 'pos': 0.492, 'compound': 0.4404} +The book was kind of good. + {'neg': 0.0, 'neu': 0.657, 'pos': 0.343, 'compound': 0.3832} +The plot was good, but the characters are uncompelling and the dialog is not great. + {'neg': 0.327, 'neu': 0.579, 'pos': 0.094, 'compound': -0.7042} +A really bad, horrible book. + {'neg': 0.791, 'neu': 0.209, 'pos': 0.0, 'compound': -0.8211} +At least it isn't a horrible book. + {'neg': 0.0, 'neu': 0.637, 'pos': 0.363, 'compound': 0.431} +:) and :D + {'neg': 0.0, 'neu': 0.124, 'pos': 0.876, 'compound': 0.7925} + + {'neg': 0.0, 'neu': 0.0, 'pos': 0.0, 'compound': 0.0} +Today sux + {'neg': 0.714, 'neu': 0.286, 'pos': 0.0, 'compound': -0.3612} +Today sux! + {'neg': 0.736, 'neu': 0.264, 'pos': 0.0, 'compound': -0.4199} +Today SUX! + {'neg': 0.779, 'neu': 0.221, 'pos': 0.0, 'compound': -0.5461} +Today kinda sux! 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VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media, and works well on texts from other domains.', + author = 'C.J. Hutto', + author_email = 'cjhutto [at] gatech [dot] edu', + license = 'MIT License: http://opensource.org/licenses/MIT', + url = 'https://github.com/cjhutto/vaderSentiment', # use the URL to the github repo + download_url = 'https://github.com/cjhutto/vaderSentiment/archive/master.zip', + keywords = ['vader', 'sentiment', 'analysis', 'opinion', 'mining', 'nlp', 'text', 'data', + 'text analysis', 'opinion analysis', 'sentiment analysis', 'text mining', 'twitter sentiment', + 'opinion mining', 'social media', 'twitter', 'social', 'media'], # arbitrary keywords + classifiers = ['Development Status :: 4 - Beta', 'Intended Audience :: Science/Research', + 'License :: OSI Approved :: MIT License', 'Natural Language :: English', + 'Programming Language :: Python :: 2.7', 'Topic :: Scientific/Engineering :: Artificial Intelligence', + 'Topic :: Scientific/Engineering :: Information Analysis', 'Topic :: Text Processing :: Linguistic', + 'Topic :: Text Processing :: General'], +) \ No newline at end of file diff --git a/vaderSentiment/__init__.py b/vaderSentiment/__init__.py new file mode 100644 index 0000000..693737f --- /dev/null +++ b/vaderSentiment/__init__.py @@ -0,0 +1 @@ +#!/usr/bin/python \ No newline at end of file diff --git a/vaderSentiment/vaderSentiment.py b/vaderSentiment/vaderSentiment.py new file mode 100644 index 0000000..93964ea --- /dev/null +++ b/vaderSentiment/vaderSentiment.py @@ -0,0 +1,363 @@ +#!/usr/bin/python +# coding: utf-8 +''' +Created on July 04, 2013 +@author: C.J. Hutto + +Citation Information + +If you use any of the VADER sentiment analysis tools +(VADER sentiment lexicon or Python code for rule-based sentiment +analysis engine) in your work or research, please cite the paper. +For example: + + Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for + Sentiment Analysis of Social Media Text. Eighth International Conference on + Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014. +''' + +import os, math, re, sys, fnmatch, string +reload(sys) + +def make_lex_dict(f): + return dict(map(lambda (w, m): (w, float(m)), [wmsr.strip().split('\t')[0:2] for wmsr in open(f) ])) + +f = 'vader_sentiment_lexicon.txt' # empirically derived valence ratings for words, emoticons, slang, swear words, acronyms/initialisms +try: + word_valence_dict = make_lex_dict(f) +except: + f = os.path.join(os.path.dirname(__file__),'vader_sentiment_lexicon.txt') + word_valence_dict = make_lex_dict(f) + +# for removing punctuation +regex_remove_punctuation = re.compile('[%s]' % re.escape(string.punctuation)) + +def sentiment(text): + """ + Returns a float for sentiment strength based on the input text. + Positive values are positive valence, negative value are negative valence. + """ + wordsAndEmoticons = str(text).split() #doesn't separate words from adjacent punctuation (keeps emoticons & contractions) + text_mod = regex_remove_punctuation.sub('', text) # removes punctuation (but loses emoticons & contractions) + wordsOnly = str(text_mod).split() + # get rid of empty items or single letter "words" like 'a' and 'I' from wordsOnly + for word in wordsOnly: + if len(word) <= 1: + wordsOnly.remove(word) + # now remove adjacent & redundant punctuation from [wordsAndEmoticons] while keeping emoticons and contractions + puncList = [".", "!", "?", ",", ";", ":", "-", "'", "\"", + "!!", "!!!", "??", "???", "?!?", "!?!", "?!?!", "!?!?"] + for word in wordsOnly: + for p in puncList: + pword = p + word + x1 = wordsAndEmoticons.count(pword) + while x1 > 0: + i = wordsAndEmoticons.index(pword) + wordsAndEmoticons.remove(pword) + wordsAndEmoticons.insert(i, word) + x1 = wordsAndEmoticons.count(pword) + + wordp = word + p + x2 = wordsAndEmoticons.count(wordp) + while x2 > 0: + i = wordsAndEmoticons.index(wordp) + wordsAndEmoticons.remove(wordp) + wordsAndEmoticons.insert(i, word) + x2 = wordsAndEmoticons.count(wordp) + # get rid of residual empty items or single letter "words" like 'a' and 'I' from wordsAndEmoticons + for word in wordsAndEmoticons: + if len(word) <= 1: + wordsAndEmoticons.remove(word) + + # remove stopwords from [wordsAndEmoticons] + #stopwords = [str(word).strip() for word in open('stopwords.txt')] + #for word in wordsAndEmoticons: + # if word in stopwords: + # wordsAndEmoticons.remove(word) + + # check for negation + negate = ["aint", "arent", "cannot", "cant", "couldnt", "darent", "didnt", "doesnt", + "ain't", "aren't", "can't", "couldn't", "daren't", "didn't", "doesn't", + "dont", "hadnt", "hasnt", "havent", "isnt", "mightnt", "mustnt", "neither", + "don't", "hadn't", "hasn't", "haven't", "isn't", "mightn't", "mustn't", + "neednt", "needn't", "never", "none", "nope", "nor", "not", "nothing", "nowhere", + "oughtnt", "shant", "shouldnt", "uhuh", "wasnt", "werent", + "oughtn't", "shan't", "shouldn't", "uh-uh", "wasn't", "weren't", + "without", "wont", "wouldnt", "won't", "wouldn't", "rarely", "seldom", "despite"] + def negated(list, nWords=[], includeNT=True): + nWords.extend(negate) + for word in nWords: + if word in list: + return True + if includeNT: + for word in list: + if "n't" in word: + return True + if "least" in list: + i = list.index("least") + if i > 0 and list[i-1] != "at": + return True + return False + + def normalize(score, alpha=15): + # normalize the score to be between -1 and 1 using an alpha that approximates the max expected value + normScore = score/math.sqrt( ((score*score) + alpha) ) + return normScore + + def wildCardMatch(patternWithWildcard, listOfStringsToMatchAgainst): + listOfMatches = fnmatch.filter(listOfStringsToMatchAgainst, patternWithWildcard) + return listOfMatches + + + def isALLCAP_differential(wordList): + countALLCAPS= 0 + for w in wordList: + if str(w).isupper(): + countALLCAPS += 1 + cap_differential = len(wordList) - countALLCAPS + if cap_differential > 0 and cap_differential < len(wordList): + isDiff = True + else: isDiff = False + return isDiff + isCap_diff = isALLCAP_differential(wordsAndEmoticons) + + b_incr = 0.293 #(empirically derived mean sentiment intensity rating increase for booster words) + b_decr = -0.293 + # booster/dampener 'intensifiers' or 'degree adverbs' http://en.wiktionary.org/wiki/Category:English_degree_adverbs + booster_dict = {"absolutely": b_incr, "amazingly": b_incr, "awfully": b_incr, "completely": b_incr, "considerably": b_incr, + "decidedly": b_incr, "deeply": b_incr, "effing": b_incr, "enormously": b_incr, + "entirely": b_incr, "especially": b_incr, "exceptionally": b_incr, "extremely": b_incr, + "fabulously": b_incr, "flipping": b_incr, "flippin": b_incr, + "fricking": b_incr, "frickin": b_incr, "frigging": b_incr, "friggin": b_incr, "fully": b_incr, "fucking": b_incr, + "greatly": b_incr, "hella": b_incr, "highly": b_incr, "hugely": b_incr, "incredibly": b_incr, + "intensely": b_incr, "majorly": b_incr, "more": b_incr, "most": b_incr, "particularly": b_incr, + "purely": b_incr, "quite": b_incr, "really": b_incr, "remarkably": b_incr, + "so": b_incr, "substantially": b_incr, + "thoroughly": b_incr, "totally": b_incr, "tremendously": b_incr, + "uber": b_incr, "unbelievably": b_incr, "unusually": b_incr, "utterly": b_incr, + "very": b_incr, + + "almost": b_decr, "barely": b_decr, "hardly": b_decr, "just enough": b_decr, + "kind of": b_decr, "kinda": b_decr, "kindof": b_decr, "kind-of": b_decr, + "less": b_decr, "little": b_decr, "marginally": b_decr, "occasionally": b_decr, "partly": b_decr, + "scarcely": b_decr, "slightly": b_decr, "somewhat": b_decr, + "sort of": b_decr, "sorta": b_decr, "sortof": b_decr, "sort-of": b_decr} + sentiments = [] + for item in wordsAndEmoticons: + v = 0 + i = wordsAndEmoticons.index(item) + if (i < len(wordsAndEmoticons)-1 and str(item).lower() == "kind" and \ + str(wordsAndEmoticons[i+1]).lower() == "of") or str(item).lower() in booster_dict: + sentiments.append(v) + continue + item_lowercase = str(item).lower() + if item_lowercase in word_valence_dict: + #get the sentiment valence + v = float(word_valence_dict[item_lowercase]) + + #check if sentiment laden word is in ALLCAPS (while others aren't) + c_incr = 0.733 #(empirically derived mean sentiment intensity rating increase for using ALLCAPs to emphasize a word) + if str(item).isupper() and isCap_diff: + if v > 0: v += c_incr + else: v -= c_incr + + #check if the preceding words increase, decrease, or negate/nullify the valence + def scalar_inc_dec(word, valence): + scalar = 0.0 + word_lower = str(word).lower() + if word_lower in booster_dict: + scalar = booster_dict[word_lower] + if valence < 0: scalar *= -1 + #check if booster/dampener word is in ALLCAPS (while others aren't) + if str(word).isupper() and isCap_diff: + if valence > 0: scalar += c_incr + else: scalar -= c_incr + return scalar + n_scalar = -0.74 + if i > 0 and str(wordsAndEmoticons[i-1]).lower() not in word_valence_dict: + s1 = scalar_inc_dec(wordsAndEmoticons[i-1], v) + v = v+s1 + if negated([wordsAndEmoticons[i-1]]): v = v*n_scalar + if i > 1 and str(wordsAndEmoticons[i-2]).lower() not in word_valence_dict: + s2 = scalar_inc_dec(wordsAndEmoticons[i-2], v) + if s2 != 0: s2 = s2*0.95 + v = v+s2 + # check for special use of 'never' as valence modifier instead of negation + if wordsAndEmoticons[i-2] == "never" and (wordsAndEmoticons[i-1] == "so" or wordsAndEmoticons[i-1] == "this"): + v = v*1.5 + # otherwise, check for negation/nullification + elif negated([wordsAndEmoticons[i-2]]): v = v*n_scalar + if i > 2 and str(wordsAndEmoticons[i-3]).lower() not in word_valence_dict: + s3 = scalar_inc_dec(wordsAndEmoticons[i-3], v) + if s3 != 0: s3 = s3*0.9 + v = v+s3 + # check for special use of 'never' as valence modifier instead of negation + if wordsAndEmoticons[i-3] == "never" and \ + (wordsAndEmoticons[i-2] == "so" or wordsAndEmoticons[i-2] == "this") or \ + (wordsAndEmoticons[i-1] == "so" or wordsAndEmoticons[i-1] == "this"): + v = v*1.25 + # otherwise, check for negation/nullification + elif negated([wordsAndEmoticons[i-3]]): v = v*n_scalar + + # check for special case idioms using a sentiment-laden keyword known to SAGE + special_case_idioms = {"the shit": 3, "the bomb": 3, "bad ass": 1.5, "yeah right": -2, + "cut the mustard": 2, "kiss of death": -1.5, "hand to mouth": -2} + # future work: consider other sentiment-laden idioms + #other_idioms = {"back handed": -2, "blow smoke": -2, "blowing smoke": -2, "upper hand": 1, "break a leg": 2, + # "cooking with gas": 2, "in the black": 2, "in the red": -2, "on the ball": 2,"under the weather": -2} + onezero = "{} {}".format(str(wordsAndEmoticons[i-1]), str(wordsAndEmoticons[i])) + twoonezero = "{} {} {}".format(str(wordsAndEmoticons[i-2]), str(wordsAndEmoticons[i-1]), str(wordsAndEmoticons[i])) + twoone = "{} {}".format(str(wordsAndEmoticons[i-2]), str(wordsAndEmoticons[i-1])) + threetwoone = "{} {} {}".format(str(wordsAndEmoticons[i-3]), str(wordsAndEmoticons[i-2]), str(wordsAndEmoticons[i-1])) + threetwo = "{} {}".format(str(wordsAndEmoticons[i-3]), str(wordsAndEmoticons[i-2])) + if onezero in special_case_idioms: v = special_case_idioms[onezero] + elif twoonezero in special_case_idioms: v = special_case_idioms[twoonezero] + elif twoone in special_case_idioms: v = special_case_idioms[twoone] + elif threetwoone in special_case_idioms: v = special_case_idioms[threetwoone] + elif threetwo in special_case_idioms: v = special_case_idioms[threetwo] + if len(wordsAndEmoticons)-1 > i: + zeroone = "{} {}".format(str(wordsAndEmoticons[i]), str(wordsAndEmoticons[i+1])) + if zeroone in special_case_idioms: v = special_case_idioms[zeroone] + if len(wordsAndEmoticons)-1 > i+1: + zeroonetwo = "{} {}".format(str(wordsAndEmoticons[i]), str(wordsAndEmoticons[i+1]), str(wordsAndEmoticons[i+2])) + if zeroonetwo in special_case_idioms: v = special_case_idioms[zeroonetwo] + + # check for booster/dampener bi-grams such as 'sort of' or 'kind of' + if threetwo in booster_dict or twoone in booster_dict: + v = v+b_decr + + # check for negation case using "least" + if i > 1 and str(wordsAndEmoticons[i-1]).lower() not in word_valence_dict \ + and str(wordsAndEmoticons[i-1]).lower() == "least": + if (str(wordsAndEmoticons[i-2]).lower() != "at" and str(wordsAndEmoticons[i-2]).lower() != "very"): + v = v*n_scalar + elif i > 0 and str(wordsAndEmoticons[i-1]).lower() not in word_valence_dict \ + and str(wordsAndEmoticons[i-1]).lower() == "least": + v = v*n_scalar + sentiments.append(v) + + # check for modification in sentiment due to contrastive conjunction 'but' + if 'but' in wordsAndEmoticons or 'BUT' in wordsAndEmoticons: + try: bi = wordsAndEmoticons.index('but') + except: bi = wordsAndEmoticons.index('BUT') + for s in sentiments: + si = sentiments.index(s) + if si < bi: + sentiments.pop(si) + sentiments.insert(si, s*0.5) + elif si > bi: + sentiments.pop(si) + sentiments.insert(si, s*1.5) + + if sentiments: + sum_s = float(sum(sentiments)) + #print sentiments, sum_s + + # check for added emphasis resulting from exclamation points (up to 4 of them) + ep_count = str(text).count("!") + if ep_count > 4: ep_count = 4 + ep_amplifier = ep_count*0.292 #(empirically derived mean sentiment intensity rating increase for exclamation points) + if sum_s > 0: sum_s += ep_amplifier + elif sum_s < 0: sum_s -= ep_amplifier + + # check for added emphasis resulting from question marks (2 or 3+) + qm_count = str(text).count("?") + qm_amplifier = 0 + if qm_count > 1: + if qm_count <= 3: qm_amplifier = qm_count*0.18 + else: qm_amplifier = 0.96 + if sum_s > 0: sum_s += qm_amplifier + elif sum_s < 0: sum_s -= qm_amplifier + + compound = normalize(sum_s) + + # want separate positive versus negative sentiment scores + pos_sum = 0.0 + neg_sum = 0.0 + neu_count = 0 + for sentiment_score in sentiments: + if sentiment_score > 0: + pos_sum += (float(sentiment_score) +1) # compensates for neutral words that are counted as 1 + if sentiment_score < 0: + neg_sum += (float(sentiment_score) -1) # when used with math.fabs(), compensates for neutrals + if sentiment_score == 0: + neu_count += 1 + + if pos_sum > math.fabs(neg_sum): pos_sum += (ep_amplifier+qm_amplifier) + elif pos_sum < math.fabs(neg_sum): neg_sum -= (ep_amplifier+qm_amplifier) + + total = pos_sum + math.fabs(neg_sum) + neu_count + pos = math.fabs(pos_sum / total) + neg = math.fabs(neg_sum / total) + neu = math.fabs(neu_count / total) + + else: + compound = 0.0; pos = 0.0; neg = 0.0; neu = 0.0 + + s = {"neg" : round(neg, 3), + "neu" : round(neu, 3), + "pos" : round(pos, 3), + "compound" : round(compound, 4)} + return s + + +if __name__ == '__main__': + # --- examples ------- + sentences = [ + "VADER is smart, handsome, and funny.", # positive sentence example + "VADER is smart, handsome, and funny!", # punctuation emphasis handled correctly (sentiment intensity adjusted) + "VADER is very smart, handsome, and funny.", # booster words handled correctly (sentiment intensity adjusted) + "VADER is VERY SMART, handsome, and FUNNY.", # emphasis for ALLCAPS handled + "VADER is VERY SMART, handsome, and FUNNY!!!",# combination of signals - VADER appropriately adjusts intensity + "VADER is VERY SMART, really handsome, and INCREDIBLY FUNNY!!!",# booster words & punctuation make this close to ceiling for score + "The book was good.", # positive sentence + "The book was kind of good.", # qualified positive sentence is handled correctly (intensity adjusted) + "The plot was good, but the characters are uncompelling and the dialog is not great.", # mixed negation sentence + "A really bad, horrible book.", # negative sentence with booster words + "At least it isn't a horrible book.", # negated negative sentence with contraction + ":) and :D", # emoticons handled + "", # an empty string is correctly handled + "Today sux", # negative slang handled + "Today sux!", # negative slang with punctuation emphasis handled + "Today SUX!", # negative slang with capitalization emphasis + "Today kinda sux! But I'll get by, lol" # mixed sentiment example with slang and constrastive conjunction "but" + ] + paragraph = "It was one of the worst movies I've seen, despite good reviews. \ + Unbelievably bad acting!! Poor direction. VERY poor production. \ + The movie was bad. Very bad movie. VERY bad movie. VERY BAD movie. VERY BAD movie!" + + from nltk import tokenize + lines_list = tokenize.sent_tokenize(paragraph) + sentences.extend(lines_list) + + tricky_sentences = [ + "Most automated sentiment analysis tools are shit.", + "VADER sentiment analysis is the shit.", + "Sentiment analysis has never been good.", + "Sentiment analysis with VADER has never been this good.", + "Warren Beatty has never been so entertaining.", + "I won't say that the movie is astounding and I wouldn't claim that the movie is too banal either.", + "I like to hate Michael Bay films, but I couldn't fault this one", + "It's one thing to watch an Uwe Boll film, but another thing entirely to pay for it", + "The movie was too good", + "This movie was actually neither that funny, nor super witty.", + "This movie doesn't care about cleverness, wit or any other kind of intelligent humor.", + "Those who find ugly meanings in beautiful things are corrupt without being charming.", + "There are slow and repetitive parts, BUT it has just enough spice to keep it interesting.", + "The script is not fantastic, but the acting is decent and the cinematography is EXCELLENT!", + "Roger Dodger is one of the most compelling variations on this theme.", + "Roger Dodger is one of the least compelling variations on this theme.", + "Roger Dodger is at least compelling as a variation on the theme.", + "they fall in love with the product", + "but then it breaks", + "usually around the time the 90 day warranty expires", + "the twin towers collapsed today", + "However, Mr. Carter solemnly argues, his client carried out the kidnapping under orders and in the ''least offensive way possible.''" + ] + sentences.extend(tricky_sentences) + for sentence in sentences: + print sentence, + ss = sentiment(sentence) + print "\t" + str(ss) + + print "\n\n Done!" diff --git a/vaderSentiment/vader_sentiment_lexicon.txt b/vaderSentiment/vader_sentiment_lexicon.txt new file mode 100644 index 0000000..7ce7b20 --- /dev/null +++ b/vaderSentiment/vader_sentiment_lexicon.txt @@ -0,0 +1,7517 @@ +$: -1.5 0.80623 [-1, -1, -1, -1, -3, -1, -3, -1, -2, -1] +%) -0.4 1.0198 [-1, 0, -1, 0, 0, -2, -1, 2, -1, 0] +%-) -1.5 1.43178 [-2, 0, -2, -2, -1, 2, -2, -3, -2, -3] +&-: -0.4 1.42829 [-3, -1, 0, 0, -1, -1, -1, 2, -1, 2] +&: -0.7 0.64031 [0, -1, -1, -1, 1, -1, -1, -1, -1, -1] +( '}{' ) 1.6 0.66332 [1, 2, 2, 1, 1, 2, 2, 1, 3, 1] +(% -0.9 0.9434 [0, 0, 1, -1, -1, -1, -2, -2, -1, -2] +('-: 2.2 1.16619 [4, 1, 4, 3, 1, 2, 3, 1, 2, 1] +(': 2.3 0.9 [1, 3, 3, 2, 2, 4, 2, 3, 1, 2] +((-: 2.1 0.53852 [2, 2, 2, 1, 2, 3, 2, 2, 3, 2] +(* 1.1 1.13578 [2, 1, 1, -1, 1, 2, 2, -1, 2, 2] +(-% -0.7 1.26886 [-1, 2, 0, -1, -1, -2, 0, 0, -3, -1] +(-* 1.3 1.26886 [4, 1, 2, 0, 2, -1, 1, 2, 1, 1] +(-: 1.6 0.8 [2, 2, 1, 3, 1, 1, 1, 3, 1, 1] +(-:0 2.8 0.87178 [3, 2, 3, 4, 3, 2, 3, 1, 4, 3] +(-:< -0.4 2.15407 [-3, 3, -1, -1, 2, -1, -2, 3, -3, -1] +(-:o 1.5 0.67082 [3, 1, 1, 2, 2, 2, 1, 1, 1, 1] +(-:O 1.5 0.67082 [3, 1, 1, 2, 2, 2, 1, 1, 1, 1] +(-:{ -0.1 1.57797 [-2, -3, 1, -2, 1, 1, 0, 0, 2, 1] +(-:|>* 1.9 0.83066 [3, 2, 2, 1, 0, 2, 3, 2, 2, 2] +(-; 1.3 1.18743 [3, 2, 3, 0, 1, -1, 1, 2, 1, 1] +(-;| 2.1 1.13578 [3, 2, 2, 4, 1, 1, 1, 4, 2, 1] +(8 2.6 1.0198 [4, 2, 1, 3, 3, 3, 3, 1, 2, 4] +(: 2.2 1.16619 [3, 1, 1, 2, 1, 2, 4, 3, 4, 1] +(:0 2.4 1.11355 [0, 2, 3, 4, 3, 2, 3, 3, 1, 3] +(:< -0.2 2.03961 [-2, -3, 1, 1, 2, -1, 2, 1, -4, 1] +(:o 2.5 0.92195 [3, 3, 1, 3, 3, 1, 2, 2, 4, 3] +(:O 2.5 0.92195 [3, 3, 1, 3, 3, 1, 2, 2, 4, 3] +(; 1.1 1.22066 [3, 1, 1, -1, 1, 2, 2, -1, 1, 2] +(;< 0.3 1.00499 [1, 2, -1, -1, 0, 0, 1, -1, 1, 1] +(= 2.2 1.16619 [3, 1, 2, 2, 1, 1, 4, 3, 4, 1] +(?: 2.1 0.83066 [2, 2, 1, 3, 2, 2, 4, 1, 2, 2] +(^: 1.5 0.67082 [1, 2, 2, 1, 3, 2, 1, 1, 1, 1] +(^; 1.5 0.5 [1, 2, 2, 1, 2, 1, 2, 1, 1, 2] +(^;0 2.0 0.7746 [2, 2, 1, 2, 1, 4, 2, 2, 2, 2] +(^;o 1.9 0.83066 [2, 2, 1, 2, 1, 4, 2, 1, 2, 2] +(o: 1.6 0.8 [2, 1, 3, 1, 1, 1, 2, 3, 1, 1] +)': -2.0 0.44721 [-2, -2, -2, -2, -1, -3, -2, -2, -2, -2] +)-': -2.1 0.53852 [-2, -2, -3, -2, -1, -2, -3, -2, -2, -2] +)-: -2.1 0.9434 [-3, -2, -4, -1, -3, -2, -2, -2, -1, -1] +)-:< -2.2 0.4 [-2, -2, -2, -2, -2, -2, -3, -3, -2, -2] +)-:{ -2.1 0.9434 [-1, -3, -2, -1, -2, -2, -3, -4, -1, -2] +): -1.8 0.87178 [-1, -3, -1, -2, -1, -3, -1, -3, -1, -2] +):< -1.9 0.53852 [-1, -3, -2, -2, -2, -1, -2, -2, -2, -2] +):{ -2.3 0.78102 [-1, -2, -3, -3, -2, -2, -4, -2, -2, -2] +);< -2.6 0.8 [-2, -2, -2, -3, -2, -3, -2, -2, -4, -4] +*) 0.6 1.42829 [1, -1, 1, -3, 1, 1, 2, 1, 1, 2] +*-) 0.3 1.61555 [1, -3, -2, 2, 1, 1, -1, 2, 1, 1] +*-: 2.1 1.51327 [2, 2, 4, 4, 2, 1, -1, 4, 1, 2] +*-; 2.4 1.62481 [2, 3, 4, 4, 2, 1, -1, 4, 1, 4] +*: 1.9 1.04403 [2, 1, 1, 3, 1, 2, 4, 3, 1, 1] +*<|:-) 1.6 1.28062 [0, 1, 3, 1, 1, 2, 3, 0, 4, 1] +*\0/* 2.3 1.00499 [2, 0, 3, 1, 3, 3, 2, 3, 3, 3] +*^: 1.6 1.42829 [2, 2, 1, 3, 2, 2, 3, 3, -1, -1] +,-: 1.2 0.4 [1, 1, 2, 1, 1, 1, 1, 1, 2, 1] +---'-;-{@ 2.3 1.18743 [0, 1, 3, 4, 2, 3, 2, 2, 2, 4] +--<--<@ 2.2 1.249 [0, 1, 2, 4, 2, 1, 3, 2, 3, 4] +.-: -1.2 0.4 [-1, -1, -1, -1, -1, -1, -2, -1, -2, -1] +..###-: -1.7 0.78102 [-2, -3, -3, -2, -1, -1, -1, -1, -1, -2] +..###: -1.9 1.04403 [-4, -1, -3, -1, -2, -2, -1, -3, -1, -1] +/-: -1.3 0.64031 [-1, -1, -1, -1, -1, -1, -1, -2, -3, -1] +/: -1.3 0.45826 [-2, -1, -1, -1, -2, -1, -1, -2, -1, -1] +/:< -1.4 0.4899 [-1, -2, -2, -1, -1, -1, -1, -1, -2, -2] +/= -0.9 0.53852 [-1, -1, -1, 0, -1, -2, -1, -1, -1, 0] +/^: -1.0 0.7746 [-2, -1, -2, 1, -1, -1, -1, -1, -1, -1] +/o: -1.4 0.66332 [0, -2, -1, -1, -2, -2, -1, -2, -1, -2] +0-8 0.1 1.44568 [2, -1, -2, 0, 2, 0, 2, 0, -2, 0] +0-| -1.2 0.4 [-2, -1, -1, -1, -1, -1, -1, -1, -2, -1] +0:) 1.9 1.04403 [2, 2, 2, 1, 0, 2, 4, 1, 3, 2] +0:-) 1.4 0.91652 [2, 1, 0, 1, 2, 3, 2, 1, 2, 0] +0:-3 1.5 0.92195 [2, 1, 0, 2, 2, 3, 2, 1, 2, 0] +0:03 1.9 1.22066 [2, 3, 2, 0, 0, 1, 4, 2, 3, 2] +0;^) 1.6 0.91652 [0, 1, 3, 1, 2, 1, 2, 1, 2, 3] +0_o -0.3 0.78102 [0, -2, 0, 1, 0, 0, -1, 0, -1, 0] +10q 2.1 1.22066 [1, 3, 1, 2, 1, 4, 3, 4, 1, 1] +1337 2.1 1.13578 [3, 1, 4, 0, 2, 3, 1, 2, 2, 3] +143 3.2 0.74833 [4, 4, 2, 3, 2, 3, 4, 3, 4, 3] +1432 2.6 0.8 [4, 3, 3, 2, 2, 4, 2, 2, 2, 2] +14aa41 2.4 0.91652 [3, 2, 2, 4, 2, 2, 1, 2, 4, 2] +182 -2.9 1.3 [-4, 0, -3, -3, -1, -3, -4, -4, -4, -3] +187 -3.1 1.22066 [-4, 0, -4, -3, -2, -4, -3, -3, -4, -4] +2g2b4g 2.8 0.6 [4, 2, 3, 2, 3, 3, 3, 3, 2, 3] +2g2bt -0.1 1.57797 [-1, 2, -1, 1, 0, 2, 0, -3, -2, 1] +2qt 2.1 0.83066 [3, 3, 3, 3, 2, 1, 2, 1, 2, 1] +3:( -2.2 0.87178 [-4, -3, -2, -3, -2, -1, -1, -2, -2, -2] +3:) 0.5 1.28452 [-2, 1, -2, 1, 1, 1, 1, 2, 1, 1] +3:-( -2.3 0.78102 [-2, -3, -2, -2, -2, -2, -4, -1, -3, -2] +3:-) -1.4 1.35647 [-1, -2, 1, 1, -2, -2, -3, -1, -3, -2] +4col -2.2 1.16619 [-2, -3, -1, -3, -4, -1, -2, -1, -4, -1] +4q -3.1 1.51327 [-3, -3, -4, -2, -4, -4, -4, 1, -4, -4] +5fs 1.5 1.11803 [1, 2, 1, 1, 2, 3, 2, 3, -1, 1] +8) 1.9 0.7 [2, 2, 2, 1, 1, 2, 2, 3, 3, 1] +8-d 1.7 0.64031 [1, 2, 0, 2, 2, 2, 2, 2, 2, 2] +8-o -0.3 0.78102 [1, -1, 0, 0, 0, -1, 0, -2, 0, 0] +86 -1.6 1.0198 [-1, -1, -1, -1, -1, -4, -1, -2, -1, -3] +8d 2.9 0.53852 [3, 3, 4, 2, 3, 3, 3, 3, 2, 3] +:###.. -2.4 0.91652 [-3, -2, -4, -3, -1, -2, -2, -3, -1, -3] +:$ -0.2 1.83303 [-2, -1, 0, 0, -1, 1, 4, -3, 1, -1] +:& -0.6 1.0198 [-2, -1, 0, 0, -1, -1, 1, -2, 1, -1] +:'( -2.2 0.74833 [-2, -1, -2, -2, -2, -2, -4, -3, -2, -2] +:') 2.3 0.78102 [3, 1, 3, 2, 2, 2, 2, 4, 2, 2] +:'-( -2.4 0.66332 [-2, -1, -2, -3, -2, -3, -3, -3, -2, -3] +:'-) 2.7 0.64031 [2, 1, 3, 3, 3, 3, 3, 3, 3, 3] +:( -1.9 1.13578 [-2, -3, -2, 0, -1, -1, -2, -3, -1, -4] +:) 2.0 1.18322 [2, 2, 1, 1, 1, 1, 4, 3, 4, 1] +:* 2.5 1.0247 [3, 2, 1, 1, 2, 3, 4, 3, 4, 2] +:-###.. -2.5 0.92195 [-3, -2, -3, -2, -4, -3, -1, -3, -1, -3] +:-& -0.5 0.92195 [-1, -1, 0, -1, -1, -1, -1, 0, 2, -1] +:-( -1.5 0.5 [-2, -1, -1, -1, -2, -2, -2, -1, -2, -1] +:-) 1.3 0.45826 [1, 1, 1, 1, 2, 1, 2, 1, 2, 1] +:-)) 2.8 1.07703 [3, 4, 4, 1, 2, 2, 4, 2, 4, 2] +:-* 1.7 0.64031 [1, 2, 1, 1, 1, 3, 2, 2, 2, 2] +:-, 1.1 0.53852 [1, 1, 1, 0, 1, 1, 1, 1, 2, 2] +:-. -0.9 0.53852 [-1, -1, 0, -1, 0, -1, -1, -1, -2, -1] +:-/ -1.2 0.6 [0, -1, -1, -1, -1, -2, -2, -1, -1, -2] +:-< -1.5 0.5 [-2, -1, -1, -2, -1, -2, -2, -1, -2, -1] +:-d 2.3 0.45826 [2, 2, 3, 3, 2, 3, 2, 2, 2, 2] +:-D 2.3 0.45826 [2, 2, 3, 3, 2, 3, 2, 2, 2, 2] +:-o 0.1 1.3 [2, -1, -2, 0, 1, 1, 2, 0, -1, -1] +:-p 1.2 0.4 [1, 2, 1, 1, 1, 1, 2, 1, 1, 1] +:-[ -1.6 0.4899 [-1, -2, -1, -2, -2, -1, -2, -1, -2, -2] +:-\ -0.9 0.3 [-1, -1, -1, -1, -1, -1, -1, 0, -1, -1] +:-c -1.3 0.45826 [-1, -1, -1, -2, -2, -1, -2, -1, -1, -1] +:-p 1.5 0.5 [1, 1, 1, 1, 1, 2, 2, 2, 2, 2] +:-| -0.7 0.64031 [-1, -1, 0, 0, 0, -1, -1, -2, 0, -1] +:-|| -2.5 0.67082 [-2, -2, -2, -3, -2, -3, -3, -2, -2, -4] +:-Þ 0.9 1.04403 [1, -1, 1, 2, 1, -1, 1, 2, 2, 1] +:/ -1.4 0.66332 [-1, -1, -1, -1, -1, -1, -3, -2, -2, -1] +:3 2.3 1.26886 [4, 1, 1, 1, 2, 2, 4, 3, 4, 1] +:< -2.1 0.7 [-3, -1, -2, -2, -2, -2, -3, -3, -2, -1] +:> 2.1 1.22066 [3, 1, 1, 1, 1, 2, 4, 3, 4, 1] +:?) 1.3 0.64031 [3, 1, 1, 1, 1, 2, 1, 1, 1, 1] +:?c -1.6 0.4899 [-1, -2, -1, -1, -2, -2, -1, -2, -2, -2] +:@ -2.5 0.80623 [-1, -3, -3, -2, -1, -3, -3, -3, -3, -3] +:d 2.3 1.1 [4, 2, 2, 1, 2, 1, 4, 3, 3, 1] +:D 2.3 1.1 [4, 2, 2, 1, 2, 1, 4, 3, 3, 1] +:l -1.7 0.9 [-1, -3, -1, -1, -1, -3, -2, -3, -1, -1] +:o -0.4 1.35647 [2, -1, -2, 0, 1, 0, -3, 0, -1, 0] +:p 1.0 0.7746 [-1, 1, 1, 1, 1, 1, 2, 1, 2, 1] +:s -1.2 0.9798 [-2, -2, -1, -1, -1, 1, -3, -1, -1, -1] +:[ -2.0 0.63246 [-2, -2, -1, -2, -2, -3, -3, -2, -1, -2] +:\ -1.3 0.45826 [-2, -1, -1, -1, -1, -1, -2, -1, -1, -2] +:] 2.2 1.16619 [3, 1, 1, 1, 3, 1, 4, 2, 2, 4] +:^) 2.1 1.13578 [3, 2, 4, 1, 1, 1, 1, 2, 4, 2] +:^* 2.6 0.91652 [2, 1, 2, 3, 4, 4, 3, 2, 3, 2] +:^/ -1.2 0.6 [-2, -1, -2, 0, -1, -1, -1, -1, -2, -1] +:^\ -1.0 0.44721 [-1, -1, -1, -1, -1, -2, 0, -1, -1, -1] +:^| -1.0 0.0 [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1] +:c -2.1 0.83066 [-3, -2, -1, -2, -2, -1, -3, -3, -3, -1] +:c) 2.0 1.18322 [2, 1, 1, 1, 1, 2, 3, 4, 4, 1] +:o) 2.1 0.9434 [1, 3, 3, 1, 1, 3, 2, 3, 1, 3] +:o/ -1.4 0.4899 [-1, -1, -1, -2, -1, -1, -2, -2, -1, -2] +:o\ -1.1 0.3 [-1, -1, -1, -2, -1, -1, -1, -1, -1, -1] +:o| -0.6 1.0198 [0, 0, 0, 0, -1, 0, 0, -3, 0, -2] +:p 1.4 0.8 [3, 1, 0, 2, 1, 1, 2, 2, 1, 1] +:{ -1.9 0.83066 [-2, -1, -1, -2, -2, -1, -3, -3, -3, -1] +:| -0.4 1.11355 [-1, -1, 0, -1, -1, -1, 1, -2, 2, 0] +:} 2.1 1.22066 [3, 1, 1, 1, 2, 1, 4, 3, 4, 1] +:Þ 1.1 0.53852 [1, 1, 1, 1, 0, 1, 1, 2, 2, 1] +;) 0.9 1.04403 [2, -1, 1, 1, 1, 1, -1, 2, 2, 1] +;-) 1.0 1.73205 [1, -2, 1, -2, 1, 4, 2, 1, 2, 2] +;-* 2.2 0.74833 [2, 2, 1, 3, 4, 2, 2, 2, 2, 2] +;-] 0.7 1.67631 [1, -2, 1, -3, 1, 2, 2, 1, 2, 2] +;d 0.8 1.249 [2, -1, 2, 1, 1, 1, -2, 2, 1, 1] +;D 0.8 1.249 [2, -1, 2, 1, 1, 1, -2, 2, 1, 1] +;] 0.6 1.11355 [1, -1, 1, 1, 1, 1, -2, 2, 1, 1] +;^) 1.4 0.91652 [2, 2, 1, 2, 1, 2, -1, 1, 2, 2] +-: -2.0 0.89443 [-2, -3, -2, -4, -1, -1, -1, -2, -2, -2] +>.< -1.3 0.45826 [-1, -2, -1, -2, -2, -1, -1, -1, -1, -1] +>: -2.1 1.13578 [-4, -1, -1, -4, -2, -3, -1, -1, -2, -2] +>:( -2.7 0.64031 [-2, -3, -2, -3, -3, -2, -4, -2, -3, -3] +>:) 0.4 1.42829 [1, 1, 2, 1, -1, -2, 1, 2, -2, 1] +>:-( -2.7 0.78102 [-3, -2, -3, -2, -4, -2, -3, -2, -2, -4] +>:-) -0.4 1.68523 [1, 2, 1, -2, -2, -1, -1, -3, -1, 2] +>:/ -1.6 0.8 [-1, -2, -1, -3, -1, -1, -1, -1, -2, -3] +>:o -1.2 1.16619 [-3, -1, -2, 0, -2, -2, 0, -1, 1, -2] +>:p 1.0 0.7746 [-1, 1, 1, 2, 1, 2, 1, 1, 1, 1] +>:[ -2.1 0.53852 [-2, -2, -2, -2, -3, -3, -2, -1, -2, -2] +>:\ -1.7 0.64031 [-1, -2, -1, -2, -2, -3, -1, -1, -2, -2] +>;( -2.9 0.7 [-3, -4, -3, -2, -2, -3, -3, -3, -2, -4] +>;) 0.1 1.04403 [-1, 1, 0, -1, 2, 0, -1, 1, 1, -1] +>_>^ 2.1 0.9434 [2, 2, 1, 4, 3, 2, 1, 3, 1, 2] +@: -2.1 0.9434 [-3, -2, -3, -1, -2, -4, -1, -2, -1, -2] +@>-->-- 2.1 1.22066 [1, 1, 0, 2, 4, 2, 4, 2, 3, 2] +@}-;-'--- 2.2 1.32665 [0, 1, 3, 2, 1, 4, 4, 1, 3, 3] +aas 2.5 0.80623 [2, 3, 3, 4, 1, 2, 3, 2, 2, 3] +aayf 2.7 0.78102 [2, 3, 2, 4, 3, 2, 2, 3, 4, 2] +afu -2.9 0.83066 [-3, -3, -3, -3, -3, -1, -4, -4, -2, -3] +alol 2.8 0.74833 [2, 2, 2, 3, 3, 2, 3, 4, 4, 3] +ambw 2.9 0.7 [2, 3, 4, 2, 3, 2, 3, 3, 4, 3] +aml 3.4 0.66332 [4, 3, 2, 4, 3, 3, 4, 4, 3, 4] +atab -1.9 1.22066 [-2, 0, -1, -2, -1, -1, -2, -4, -4, -2] +awol -1.3 0.78102 [0, -1, -1, -1, -1, -1, -2, -2, -3, -1] +ayc 0.2 0.9798 [0, 1, -1, 1, 0, 1, 0, -1, 2, -1] +ayor -1.2 0.6 [-1, -1, -2, -2, -1, -1, -1, 0, -2, -1] +aug-00 0.3 1.18743 [2, 0, -2, 0, 0, 1, -1, 2, 1, 0] +bfd -2.7 0.78102 [-3, -2, -4, -2, -3, -2, -3, -2, -4, -2] +bfe -2.6 1.35647 [-3, -3, -4, -2, -3, -2, 1, -3, -4, -3] +bff 2.9 0.83066 [3, 3, 4, 2, 4, 2, 2, 3, 4, 2] +bffn 1.0 0.89443 [2, 1, -1, 1, 0, 1, 2, 1, 2, 1] +bl 2.3 1.1 [2, 1, 4, 1, 2, 2, 4, 3, 1, 3] +bsod -2.2 1.07703 [-1, -4, -3, -3, 0, -2, -3, -2, -2, -2] +btd -2.1 0.83066 [-1, -2, -3, -3, -3, -1, -3, -2, -1, -2] +btdt -0.1 1.22066 [0, -1, 0, -1, 0, 3, 1, -1, -1, -1] +bz 0.4 1.35647 [-1, 0, 0, 0, 4, 1, -1, 1, 0, 0] +b^d 2.6 0.8 [3, 2, 2, 4, 3, 1, 3, 3, 3, 2] +cwot -2.3 0.45826 [-3, -2, -2, -2, -2, -3, -2, -3, -2, -2] +d-': -2.5 0.67082 [-3, -3, -2, -2, -2, -4, -2, -3, -2, -2] +d8 -3.2 0.6 [-3, -3, -3, -3, -4, -4, -2, -3, -3, -4] +d: -2.9 0.83066 [-3, -3, -3, -3, -2, -4, -1, -3, -3, -4] +d:< -3.2 0.9798 [-4, -4, -4, -1, -3, -3, -4, -2, -3, -4] +d; -2.9 0.83066 [-1, -3, -3, -3, -3, -4, -2, -3, -3, -4] +d= -3.0 0.89443 [-4, -3, -3, -3, -2, -4, -1, -3, -3, -4] +doa -2.3 1.00499 [-2, -3, -3, -2, -2, -2, -4, 0, -2, -3] +dx -3.0 0.63246 [-3, -2, -3, -3, -4, -3, -4, -2, -3, -3] +ez 1.5 0.67082 [3, 2, 2, 1, 1, 1, 2, 1, 1, 1] +fav 2.4 0.91652 [3, 1, 3, 2, 2, 3, 1, 2, 3, 4] +fcol -1.8 0.74833 [-2, -2, -1, -2, -1, -2, -1, -3, -3, -1] +ff 1.8 1.249 [4, 2, 1, 2, 1, 3, 3, 0, 2, 0] +ffs -2.8 0.9798 [-2, -2, -3, -3, -2, -4, -4, -4, -1, -3] +fkm -2.4 1.35647 [-4, -1, -4, -2, -2, -3, -1, 0, -3, -4] +foaf 1.8 1.249 [2, 1, 2, 0, 4, 1, 1, 1, 2, 4] +ftw 2.0 0.7746 [2, 1, 1, 2, 2, 2, 3, 3, 1, 3] +fu -3.7 0.45826 [-3, -4, -4, -3, -3, -4, -4, -4, -4, -4] +fubar -3.0 1.09545 [-4, -3, -3, -4, -3, -3, -3, -4, 0, -3] +fwb 2.5 1.43178 [2, 3, 4, 0, 1, 2, 4, 1, 4, 4] +fyi 0.8 1.66132 [0, 1, 0, -1, 0, 0, 4, 4, 0, 0] +fysa 0.4 0.91652 [0, 0, 0, 1, 0, 3, 0, 0, 0, 0] +g1 1.4 0.4899 [2, 1, 1, 1, 2, 1, 2, 1, 1, 2] +gg 1.2 0.74833 [0, 2, 2, 1, 0, 1, 2, 2, 1, 1] +gga 1.7 0.45826 [2, 2, 1, 2, 2, 1, 2, 2, 1, 2] +gigo -0.6 1.11355 [-2, -1, 1, 0, 0, 0, -1, -2, -2, 1] +gj 2.0 1.0 [2, 1, 2, 1, 1, 3, 4, 2, 3, 1] +gl 1.3 0.64031 [1, 1, 1, 1, 3, 1, 1, 2, 1, 1] +gla 2.5 0.92195 [1, 2, 2, 4, 2, 4, 2, 3, 3, 2] +gn 1.2 0.74833 [1, 1, 1, 1, 3, 1, 1, 2, 1, 0] +gr8 2.7 0.78102 [1, 3, 3, 4, 3, 2, 3, 2, 3, 3] +grrr -0.4 1.42829 [-2, -1, 0, 1, -2, -1, -1, 3, 0, -1] +gt 1.1 0.53852 [1, 1, 1, 1, 1, 1, 2, 1, 0, 2] +h&k 2.3 0.78102 [2, 2, 2, 3, 4, 2, 3, 2, 1, 2] +hagd 2.2 0.87178 [2, 2, 3, 2, 1, 3, 4, 1, 2, 2] +hagn 2.2 0.87178 [2, 2, 3, 2, 1, 3, 4, 1, 2, 2] +hago 1.2 0.4 [1, 2, 1, 1, 1, 2, 1, 1, 1, 1] +hak 1.9 0.7 [3, 1, 2, 2, 1, 2, 3, 2, 1, 2] +hand 2.2 0.87178 [2, 2, 1, 3, 2, 3, 4, 1, 2, 2] +hho1/2k 1.4 1.11355 [1, -1, 2, 3, 1, 1, 1, 2, 3, 1] +hhoj 2.0 1.09545 [4, 2, 1, 1, 2, 1, 1, 4, 2, 2] +hhok 0.9 0.9434 [1, 2, 1, 0, -1, 0, 2, 1, 1, 2] +hugz 2.0 0.7746 [2, 3, 1, 3, 1, 3, 1, 2, 2, 2] +hi5 1.9 0.53852 [2, 2, 2, 1, 3, 2, 1, 2, 2, 2] +idk -0.4 0.66332 [0, 0, 0, 0, -1, -2, 0, 0, 0, -1] +ijs 0.7 1.84662 [0, -1, 0, -1, 0, 4, 0, 4, -1, 2] +ilu 3.4 0.66332 [3, 4, 3, 4, 2, 3, 4, 3, 4, 4] +iluaaf 2.7 1.1 [3, 3, 3, 2, 3, 0, 4, 3, 2, 4] +ily 3.4 0.66332 [3, 4, 3, 4, 2, 3, 4, 3, 4, 4] +ily2 2.6 0.66332 [3, 2, 3, 2, 3, 2, 3, 4, 2, 2] +iou 0.7 1.34536 [0, 0, -1, 2, 0, 0, 0, 4, 1, 1] +iyq 2.3 1.18743 [3, 3, 1, 1, 2, 1, 4, 4, 3, 1] +j/j 2.0 1.34164 [4, 1, 1, 1, 1, 4, 4, 1, 2, 1] +j/k 1.6 1.2 [1, 2, 1, 3, 0, 0, 2, 2, 1, 4] +j/p 1.4 0.66332 [1, 1, 0, 2, 1, 2, 2, 2, 1, 2] +j/t -0.2 1.46969 [1, -1, -1, -2, 1, 1, 2, -2, 1, -2] +j/w 1.0 1.0 [1, 1, 1, 3, 0, 0, 0, 2, 0, 2] +j4f 1.4 0.8 [2, 1, 1, 0, 3, 1, 1, 1, 2, 2] +j4g 1.7 1.18743 [1, 4, 1, 1, 3, 1, 3, 0, 2, 1] +jho 0.8 0.4 [1, 1, 1, 1, 0, 1, 1, 1, 0, 1] +jhomf 1.0 0.63246 [1, 1, 1, 0, 1, 0, 2, 2, 1, 1] +jj 1.0 0.63246 [1, 1, 1, 1, 2, 0, 2, 1, 1, 0] +jk 0.9 1.22066 [1, 0, 0, 1, 0, 0, 2, 1, 4, 0] +jp 0.8 0.74833 [1, 1, 1, 0, 2, 0, 2, 0, 1, 0] +jt 0.9 0.83066 [1, 1, 0, 2, 2, 0, 2, 0, 1, 0] +jw 1.6 1.68523 [3, 0, 0, 0, 0, 0, 3, 4, 2, 4] +jealz -1.2 0.9798 [-1, -1, -1, 1, -2, -2, -1, -3, -1, -1] +k4y 2.3 1.00499 [2, 1, 1, 2, 4, 2, 3, 4, 2, 2] +kfy 2.3 0.64031 [2, 2, 2, 1, 3, 2, 3, 3, 2, 3] +kia -3.2 0.6 [-3, -3, -3, -4, -3, -2, -3, -3, -4, -4] +kk 1.5 1.0247 [2, 1, 0, 0, 1, 2, 3, 3, 2, 1] +kmuf 2.2 1.4 [2, 2, 2, 3, 4, 3, -1, 1, 4, 2] +l 2.0 0.7746 [2, 1, 2, 3, 2, 3, 1, 3, 2, 1] +l&r 2.2 0.74833 [3, 2, 2, 3, 1, 3, 1, 3, 2, 2] +laoj 1.3 1.73494 [1, -2, -1, 3, 3, 2, 4, 1, 1, 1] +lmao 2.0 1.18322 [3, 0, 3, 0, 3, 1, 3, 2, 3, 2] +lmbao 1.8 1.77764 [3, 2, 2, 2, 1, 3, -3, 2, 4, 2] +lmfao 2.5 1.28452 [3, 2, 3, 3, 3, -1, 4, 2, 3, 2] +lmso 2.7 0.78102 [3, 3, 4, 3, 3, 1, 3, 3, 2, 2] +lol 2.9 0.83066 [4, 2, 2, 2, 4, 2, 3, 3, 4, 3] +lolz 2.7 0.78102 [2, 3, 3, 2, 2, 4, 4, 3, 2, 2] +lts 1.6 0.66332 [1, 1, 2, 2, 1, 3, 1, 1, 2, 2] +ly 2.6 0.91652 [2, 2, 1, 3, 4, 4, 3, 2, 2, 3] +ly4e 2.7 0.78102 [3, 3, 3, 2, 1, 3, 3, 4, 2, 3] +lya 3.3 0.78102 [3, 4, 4, 4, 2, 2, 3, 4, 3, 4] +lyb 3.0 0.63246 [3, 3, 4, 3, 2, 3, 2, 4, 3, 3] +lyl 3.1 0.7 [4, 3, 4, 3, 2, 3, 3, 2, 4, 3] +lylab 2.7 0.78102 [3, 3, 3, 1, 3, 4, 2, 2, 3, 3] +lylas 2.6 0.8 [3, 3, 3, 1, 3, 4, 2, 2, 2, 3] +lylb 1.6 1.56205 [2, 2, 3, -2, 4, 1, 3, 1, 1, 1] +m8 1.4 1.0198 [3, 0, 1, 0, 1, 3, 2, 2, 1, 1] +mia -1.2 0.4 [-2, -1, -1, -2, -1, -1, -1, -1, -1, -1] +mml 2.0 1.0 [1, 1, 2, 3, 3, 2, 1, 2, 4, 1] +mofo -2.4 2.2 [-4, -4, -4, 0, -3, -2, -2, -4, 3, -4] +muah 2.8 1.07703 [1, 2, 4, 4, 4, 2, 4, 2, 2, 3] +mubar -1.0 2.36643 [-4, -2, -3, -2, -2, -2, 1, 4, 2, -2] +musm 0.9 2.07123 [-1, 1, 1, 1, 4, 3, 1, -4, 1, 2] +mwah 2.5 0.80623 [2, 2, 2, 4, 2, 3, 2, 2, 4, 2] +n1 1.9 1.04403 [1, 1, 3, 2, 2, 3, 4, 1, 1, 1] +nbd 1.3 1.34536 [2, 1, 0, 0, 0, 4, 2, 0, 3, 1] +nbif -0.5 0.67082 [-1, -2, 0, 0, 0, 0, -1, -1, 0, 0] +nfc -2.7 0.9 [-3, -2, -2, -3, -1, -2, -4, -3, -4, -3] +nfw -2.4 1.0198 [-2, -2, -1, -3, -1, -2, -4, -3, -4, -2] +nh 2.2 0.6 [2, 2, 2, 2, 1, 3, 3, 3, 2, 2] +nimby -0.8 0.6 [0, 0, -1, 0, -1, -2, -1, -1, -1, -1] +nimjd -0.7 0.78102 [0, -2, -1, -2, 0, -1, 0, 0, 0, -1] +nimq -0.2 0.6 [0, 0, 0, 0, 0, 0, 0, 0, -2, 0] +nimy -1.4 1.68523 [-1, -2, -3, -2, -1, 2, -3, 0, 0, -4] +nitl -1.5 0.92195 [-1, -1, -2, -3, -1, -3, -1, -2, 0, -1] +nme -2.1 1.13578 [-1, -2, -2, -1, -4, -2, -3, -3, 0, -3] +noyb -0.7 1.67631 [-1, -2, 0, -1, -1, -2, -2, -1, 4, -1] +np 1.4 1.0198 [0, 1, 1, 1, 1, 2, 2, 4, 1, 1] +ntmu 1.4 0.66332 [1, 1, 0, 1, 2, 2, 2, 2, 2, 1] +o-8 -0.5 1.5 [2, -1, 0, 0, -2, -2, 0, -2, 2, -2] +o-: -0.3 1.18743 [2, -1, 0, 0, -1, -2, 0, -2, 1, 0] +o-| -1.1 0.53852 [-1, -1, -1, 0, -1, -1, -1, -2, -2, -1] +o.o -0.6 0.8 [-1, -1, -2, 0, 1, 0, -1, 0, -1, -1] +O.o -0.6 0.8 [-1, -1, -2, 0, 1, 0, -1, 0, -1, -1] +o.O -0.6 0.8 [-1, -1, -2, 0, 1, 0, -1, 0, -1, -1] +o: -0.2 0.87178 [-1, 0, -1, -2, 0, 1, 0, 1, 0, 0] +o:) 1.5 0.67082 [3, 1, 1, 2, 2, 2, 1, 1, 1, 1] +o:-) 2.0 1.18322 [1, 4, 1, 2, 4, 1, 1, 2, 3, 1] +o:-3 2.2 0.9798 [1, 4, 2, 3, 3, 2, 1, 2, 3, 1] +o:3 2.3 0.78102 [3, 3, 2, 2, 1, 2, 4, 2, 2, 2] +o:< -0.3 1.1 [-1, -1, -2, 0, -1, 0, 1, 2, 0, -1] +o;^) 1.6 0.8 [1, 2, 1, 2, 1, 2, 2, 0, 3, 2] +ok 1.6 1.42829 [0, 0, 1, 1, 1, 4, 3, 4, 1, 1] +o_o -0.5 0.92195 [0, -1, 0, -2, -2, 0, -1, 1, 0, 0] +O_o -0.5 0.92195 [0, -1, 0, -2, -2, 0, -1, 1, 0, 0] +o_O -0.5 0.92195 [0, -1, 0, -2, -2, 0, -1, 1, 0, 0] +pita -2.4 1.2 [-2, -1, -1, -4, -4, -2, -4, -2, -3, -1] +pls 0.3 0.45826 [0, 1, 1, 1, 0, 0, 0, 0, 0, 0] +plz 0.3 0.45826 [0, 1, 1, 1, 0, 0, 0, 0, 0, 0] +pmbi 0.8 1.32665 [3, 0, 0, 1, 1, -2, 2, 2, 0, 1] +pmfji 0.3 0.78102 [0, 0, 1, 0, 2, -1, 0, 1, 0, 0] +pmji 0.7 1.00499 [1, 2, 0, -1, 0, 0, 2, 2, 1, 0] +po -2.6 0.91652 [-2, -3, -4, -3, -3, -3, -1, -3, -1, -3] +ptl 2.6 1.11355 [3, 4, 2, 4, 1, 2, 3, 1, 4, 2] +pu -1.1 1.3 [-3, -1, -3, -2, -1, -1, -1, -1, 1, 1] +qq -2.2 0.6 [-2, -2, -1, -3, -3, -2, -2, -3, -2, -2] +qt 1.8 0.6 [2, 2, 1, 2, 1, 3, 2, 1, 2, 2] +r&r 2.4 1.0198 [2, 4, 2, 3, 1, 4, 2, 2, 1, 3] +rofl 2.7 0.78102 [3, 2, 2, 2, 4, 4, 2, 3, 3, 2] +roflmao 2.5 1.11803 [4, 2, 2, 4, 1, 1, 2, 4, 3, 2] +rotfl 2.6 0.66332 [3, 2, 3, 3, 1, 3, 3, 3, 2, 3] +rotflmao 2.8 1.07703 [4, 3, 2, 4, 1, 1, 4, 3, 3, 3] +rotflmfao 2.5 1.11803 [3, 4, 1, 3, 3, 3, 0, 3, 2, 3] +rotflol 3.0 1.09545 [1, 4, 4, 4, 2, 2, 2, 3, 4, 4] +rotgl 2.9 0.7 [4, 3, 2, 2, 3, 3, 3, 2, 4, 3] +rotglmao 1.8 2.4 [3, 3, 4, 3, -1, 1, 4, -4, 2, 3] +s: -1.1 0.83066 [-1, -1, -2, -2, -1, -1, -2, -1, 1, -1] +sapfu -1.1 1.57797 [-2, 0, -3, -1, -1, 1, -2, 2, -2, -3] +sete 2.8 0.87178 [3, 3, 3, 2, 3, 3, 4, 4, 1, 2] +sfete 2.7 0.78102 [4, 3, 3, 3, 2, 4, 2, 2, 2, 2] +sgtm 2.4 1.0198 [2, 1, 1, 2, 3, 3, 2, 2, 4, 4] +slap 0.6 2.15407 [2, -1, 1, -1, 0, 4, -3, 4, 1, -1] +slaw 2.1 1.04403 [3, 2, 0, 2, 2, 2, 3, 1, 4, 2] +smh -1.3 0.64031 [-2, -1, 0, -1, -1, -2, -2, -1, -2, -1] +snafu -2.5 1.11803 [-3, -4, -3, -3, -1, 0, -2, -3, -3, -3] +sob -2.8 0.9798 [-3, -4, -3, -2, -2, -1, -2, -4, -4, -3] +swak 2.3 1.00499 [2, 2, 2, 1, 4, 2, 3, 2, 1, 4] +tgif 2.3 1.34536 [1, 3, 3, 3, -1, 2, 4, 2, 3, 3] +thks 1.4 0.4899 [1, 2, 1, 2, 1, 2, 1, 1, 2, 1] +thx 1.5 0.92195 [0, 1, 3, 2, 1, 2, 1, 1, 3, 1] +tia 2.3 0.9 [3, 1, 2, 1, 4, 3, 2, 3, 2, 2] +tmi -0.3 1.61555 [-1, -1, 2, -1, 1, -2, -2, -1, 3, -1] +tnx 1.1 0.53852 [2, 1, 1, 0, 1, 1, 2, 1, 1, 1] +true 1.8 1.32665 [2, 1, 1, 0, 1, 4, 3, 1, 4, 1] +tx 1.5 0.92195 [3, 2, 1, 0, 2, 1, 3, 1, 1, 1] +txs 1.1 0.7 [1, 2, 0, 1, 2, 0, 1, 2, 1, 1] +ty 1.6 0.66332 [1, 2, 3, 1, 2, 2, 1, 2, 1, 1] +tyvm 2.5 1.11803 [2, 2, 1, 3, 1, 4, 2, 4, 2, 4] +urw 1.9 1.13578 [1, 2, 1, 2, 4, 2, 4, 1, 1, 1] +vbg 2.1 1.75784 [2, 3, 3, 3, 3, -3, 3, 2, 2, 3] +vbs 3.1 0.53852 [2, 3, 3, 3, 4, 4, 3, 3, 3, 3] +vip 2.3 1.00499 [2, 1, 1, 3, 4, 2, 2, 4, 2, 2] +vwd 2.6 0.91652 [4, 2, 4, 2, 1, 3, 3, 2, 3, 2] +vwp 2.1 0.7 [3, 1, 2, 2, 3, 2, 2, 3, 1, 2] +wag -0.2 0.74833 [-1, 0, 0, 0, 0, 0, -2, 1, 0, 0] +wd 2.7 1.1 [3, 1, 4, 3, 4, 2, 1, 3, 2, 4] +wilco 0.9 0.9434 [1, 3, 1, 0, 1, 0, 2, 1, 0, 0] +wp 1.0 0.0 [1, 1, 1, 1, 1, 1, 1, 1, 1, 1] +wtf -2.8 0.74833 [-4, -3, -2, -3, -2, -2, -2, -4, -3, -3] +wtg 2.1 0.7 [1, 3, 2, 3, 2, 2, 2, 1, 2, 3] +wth -2.4 0.4899 [-2, -3, -2, -3, -2, -2, -2, -3, -3, -2] +x-d 2.7 0.78102 [1, 3, 4, 2, 3, 3, 3, 2, 3, 3] +x-p 1.8 0.87178 [2, 1, 3, 1, 3, 1, 3, 1, 2, 1] +xd 2.7 0.9 [1, 4, 4, 3, 2, 2, 3, 3, 2, 3] +xlnt 3.0 0.89443 [4, 3, 3, 1, 4, 4, 3, 3, 3, 2] +xoxo 3.0 0.7746 [2, 2, 4, 2, 3, 3, 4, 3, 3, 4] +xoxozzz 2.3 0.78102 [3, 1, 2, 2, 2, 2, 3, 2, 4, 2] +xp 1.2 0.4 [1, 1, 1, 1, 2, 1, 2, 1, 1, 1] +xqzt 1.6 1.42829 [0, 2, 1, 2, 4, -1, 3, 1, 1, 3] +xtc 0.8 1.93907 [2, 0, -3, 3, 3, -1, 3, 1, -1, 1] +yolo 1.1 0.83066 [0, 1, 1, 2, 1, 1, 1, 3, 0, 1] +yoyo 0.4 1.85472 [-1, 0, -1, -1, 4, 2, -2, 2, 2, -1] +yvw 1.6 0.4899 [1, 2, 1, 1, 2, 2, 2, 1, 2, 2] +yw 1.8 1.32665 [1, 1, 1, 4, 1, 1, 4, 0, 3, 2] +ywia 2.5 1.11803 [3, 2, 3, 4, 1, 1, 1, 3, 3, 4] +zzz -1.2 0.87178 [0, -1, 0, -1, -3, -1, -1, -2, -2, -1] +[-; 0.5 1.28452 [1, -1, -1, 1, 1, 1, 2, -2, 2, 1] +[: 1.3 0.45826 [1, 1, 2, 1, 2, 2, 1, 1, 1, 1] +[; 1.0 1.34164 [2, 1, 2, 2, 1, 2, 2, -2, -1, 1] +[= 1.7 0.64031 [2, 2, 1, 1, 1, 2, 2, 3, 2, 1] +\-: -1.0 1.18322 [-3, -1, -1, -1, -1, -1, 2, -2, -1, -1] +\: -1.0 0.0 [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1] +\:< -1.7 1.18743 [-1, -3, -2, -2, -3, -3, -2, -1, 1, -1] +\= -1.1 0.3 [-1, -1, -1, -1, -1, -1, -1, -2, -1, -1] +\^: -1.3 0.45826 [-1, -1, -1, -2, -1, -1, -1, -2, -2, -1] +\o/ 2.2 0.9798 [2, 1, 1, 2, 4, 2, 2, 4, 2, 2] +\o: -1.2 0.4 [-1, -1, -1, -1, -2, -1, -1, -2, -1, -1] +]-: -2.1 0.53852 [-2, -3, -3, -2, -2, -2, -1, -2, -2, -2] +]: -1.6 0.66332 [-1, -2, -1, -2, -3, -2, -1, -1, -1, -2] +]:< -2.5 0.80623 [-2, -2, -2, -3, -4, -2, -2, -2, -2, -4] +^<_< 1.4 1.11355 [3, 1, 3, 2, 1, 1, 1, -1, 2, 1] +^urs -2.8 0.6 [-2, -3, -3, -2, -3, -3, -2, -3, -4, -3] +abandon -1.9 0.53852 [-1, -2, -2, -2, -2, -3, -2, -2, -1, -2] +abandoned -2.0 1.09545 [-1, -1, -3, -2, -1, -4, -1, -3, -3, -1] +abandoner -1.9 0.83066 [-1, -1, -3, -2, -1, -3, -1, -2, -3, -2] +abandoners -1.9 0.83066 [-2, -3, -2, -3, -2, -1, -2, -2, 0, -2] +abandoning -1.6 0.8 [-3, -2, -3, -2, -1, -1, -1, -1, -1, -1] +abandonment -2.4 1.0198 [-4, -2, -1, -4, -2, -1, -2, -3, -3, -2] +abandonments -1.7 0.45826 [-2, -1, -2, -2, -1, -2, -1, -2, -2, -2] +abandons -1.3 0.9 [-2, -1, -1, -2, -1, -2, -1, -2, 1, -2] +abducted -2.3 1.18743 [-3, -1, 0, -3, -1, -3, -4, -2, -3, -3] +abduction -2.8 0.87178 [-4, -3, -3, -4, -1, -3, -2, -2, -3, -3] +abductions -2.0 1.41421 [-3, -4, -1, -3, -1, -3, 1, -2, -1, -3] +abhor -2.0 1.09545 [-3, -3, -1, -1, -2, -1, -3, -3, 0, -3] +abhorred -2.4 1.49666 [-4, -4, 0, -3, -2, -1, -4, -3, -3, 0] +abhorrent -3.1 1.3 [-4, -4, -4, -2, 0, -4, -2, -3, -4, -4] +abhors -2.9 1.51327 [0, -4, -3, -3, -4, -4, 0, -4, -3, -4] +abilities 1.0 0.63246 [1, 2, 0, 1, 0, 1, 1, 1, 1, 2] +ability 1.3 0.64031 [1, 1, 1, 0, 1, 2, 2, 2, 2, 1] +aboard 0.1 0.3 [0, 0, 0, 0, 1, 0, 0, 0, 0, 0] +absentee -1.1 0.53852 [-1, -1, 0, -2, -1, -1, -2, -1, -1, -1] +absentees -0.8 0.6 [-1, 0, 0, -1, -1, 0, -2, -1, -1, -1] +absolve 1.2 1.46969 [2, -3, 2, 2, 1, 1, 2, 1, 2, 2] +absolved 1.5 0.92195 [3, 1, 2, 1, 0, 2, 3, 1, 1, 1] +absolves 1.3 1.00499 [3, 1, 1, 0, 0, 2, 3, 1, 1, 1] +absolving 1.6 0.8 [3, 1, 2, 1, 1, 2, 3, 1, 1, 1] +abuse -3.2 0.6 [-4, -2, -3, -4, -3, -4, -3, -3, -3, -3] +abused -2.3 0.64031 [-2, -2, -3, -2, -2, -4, -2, -2, -2, -2] +abuser -2.6 0.4899 [-3, -2, -3, -3, -2, -3, -2, -2, -3, -3] +abusers -2.6 1.0198 [-2, -3, -3, -3, -3, -2, -3, -4, -3, 0] +abuses -2.6 0.66332 [-3, -2, -3, -3, -3, -3, -1, -2, -3, -3] +abusing -2.0 1.41421 [-1, -2, -2, -4, -4, -2, -3, -1, 1, -2] +abusive -3.2 0.74833 [-4, -3, -3, -4, -4, -3, -4, -2, -3, -2] +abusively -2.8 0.6 [-3, -4, -3, -2, -3, -2, -2, -3, -3, -3] +abusiveness -2.5 0.92195 [-2, -4, -2, -3, -2, -3, -4, -2, -1, -2] +abusivenesses -3.0 0.63246 [-3, -3, -4, -3, -4, -2, -2, -3, -3, -3] +accept 1.6 0.91652 [2, 1, 2, 1, 1, 2, 4, 1, 1, 1] +acceptabilities 1.6 0.66332 [0, 2, 2, 2, 1, 2, 2, 2, 1, 2] +acceptability 1.1 0.53852 [1, 0, 1, 2, 1, 2, 1, 1, 1, 1] +acceptable 1.3 0.45826 [1, 2, 1, 1, 1, 2, 1, 1, 2, 1] +acceptableness 1.3 0.9 [1, 0, 2, 1, 2, 1, 1, 0, 2, 3] +acceptably 1.5 0.67082 [3, 2, 1, 1, 1, 2, 1, 1, 2, 1] +acceptance 2.0 0.63246 [3, 1, 3, 2, 1, 2, 2, 2, 2, 2] +acceptances 1.7 0.78102 [3, 1, 1, 1, 2, 2, 1, 2, 3, 1] +acceptant 1.6 0.8 [0, 1, 2, 2, 2, 1, 2, 1, 3, 2] +acceptation 1.3 0.78102 [0, 1, 2, 1, 1, 1, 1, 3, 2, 1] +acceptations 0.9 0.83066 [1, 2, 0, 2, 0, 1, 0, 2, 1, 0] +accepted 1.1 0.3 [1, 1, 1, 1, 1, 2, 1, 1, 1, 1] +accepting 1.6 0.66332 [2, 2, 2, 1, 1, 2, 1, 3, 1, 1] +accepts 1.3 0.45826 [1, 2, 1, 1, 1, 2, 2, 1, 1, 1] +accident -2.1 0.83066 [-2, -2, -1, -3, -4, -2, -2, -1, -2, -2] +accidental -0.3 0.45826 [-1, -1, 0, 0, 0, 0, 0, 0, -1, 0] +accidentally -1.4 0.91652 [-2, 0, -2, 0, -3, -1, -1, -1, -2, -2] +accidents -1.3 0.78102 [-1, -1, -1, -1, -2, 0, -3, -1, -2, -1] +accomplish 1.8 0.6 [1, 2, 3, 2, 2, 2, 1, 1, 2, 2] +accomplished 1.9 0.53852 [2, 2, 2, 1, 2, 2, 3, 1, 2, 2] +accomplishes 1.7 0.9 [2, 2, 1, 0, 2, 3, 3, 1, 1, 2] +accusation -1.0 1.09545 [-1, -1, -2, -2, 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2, 1, 2, 2, 1, 1, 1] +admiring 1.6 0.8 [1, 2, 1, 1, 3, 3, 2, 1, 1, 1] +admiringly 2.3 0.64031 [1, 3, 3, 2, 2, 2, 2, 3, 3, 2] +admit 0.8 1.07703 [0, 0, 0, 0, 0, 1, 3, 2, 2, 0] +admits 1.2 0.87178 [1, 2, 2, 2, 0, 0, 1, 2, 0, 2] +admitted 0.4 0.66332 [0, 1, 0, 1, 0, 0, 2, 0, 0, 0] +admonished -1.9 0.9434 [-2, -2, -2, -1, -2, -3, -1, -1, -1, -4] +adopt 0.7 0.64031 [0, 0, 1, 1, 1, 0, 1, 0, 1, 2] +adopts 0.7 0.64031 [0, 0, 1, 2, 1, 0, 1, 1, 0, 1] +adorability 2.2 0.74833 [2, 2, 2, 2, 1, 2, 3, 2, 4, 2] +adorable 2.2 0.6 [3, 2, 2, 3, 2, 2, 1, 3, 2, 2] +adorableness 2.5 0.67082 [2, 3, 3, 2, 3, 2, 1, 3, 3, 3] +adorably 2.1 0.7 [3, 1, 2, 3, 2, 2, 1, 3, 2, 2] +adoration 2.9 0.7 [3, 3, 3, 2, 3, 3, 4, 2, 4, 2] +adorations 2.2 0.87178 [2, 2, 3, 1, 3, 1, 3, 3, 1, 3] +adore 2.6 0.91652 [3, 3, 1, 2, 3, 3, 3, 4, 1, 3] +adored 1.8 0.87178 [2, 3, 3, 2, 2, 1, 1, 0, 2, 2] +adorer 1.7 1.1 [2, 4, 3, 1, 2, 1, 1, 0, 2, 1] +adorers 2.1 0.7 [3, 2, 1, 2, 2, 2, 3, 2, 3, 1] +adores 1.6 0.66332 [2, 1, 3, 2, 2, 1, 1, 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2, 2] +agreeableness 1.8 0.9798 [2, 3, 2, 1, 1, 1, 4, 1, 2, 1] +agreeablenesses 1.3 0.45826 [2, 1, 1, 2, 1, 2, 1, 1, 1, 1] +agreeably 1.6 0.4899 [1, 1, 2, 1, 1, 2, 2, 2, 2, 2] +agreed 1.1 0.53852 [1, 1, 2, 1, 1, 2, 1, 0, 1, 1] +agreeing 1.4 0.4899 [1, 1, 1, 1, 2, 1, 1, 2, 2, 2] +agreement 2.2 0.74833 [2, 1, 1, 3, 3, 3, 2, 2, 2, 3] +agreements 1.1 1.13578 [0, 1, 0, 1, 1, 2, 1, 0, 4, 1] +agrees 0.8 1.4 [1, 1, 1, 1, 3, 1, -3, 1, 1, 1] +alarm -1.4 0.91652 [-1, -1, -2, -2, -2, -2, -1, 1, -2, -2] +alarmed -1.4 0.4899 [-2, -1, -2, -2, -1, -1, -1, -2, -1, -1] +alarming -0.5 1.62788 [-1, 1, -1, 1, -3, 1, -2, -3, 1, 1] +alarmingly -2.6 0.91652 [-3, -3, -4, -3, -2, -1, -3, -3, -1, -3] +alarmism -0.3 1.26886 [-2, 0, -2, -1, 0, 1, 1, -1, 2, -1] +alarmists -1.1 1.3 [-1, -2, -3, -2, -1, -2, 1, 1, 0, -2] +alarms -1.1 1.04403 [-2, 0, -2, -1, 0, 0, 0, -1, -2, -3] +alas -1.1 1.22066 [-1, -2, -1, 0, -1, 0, 1, -3, -1, -3] +alert 1.2 0.87178 [1, 2, 0, 1, 0, 2, 2, 0, 2, 2] +alienation -1.1 1.51327 [-3, -2, 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-1, -1, -1, -1, 0, -1, 0, -2, -1] +cancelled -1.0 0.44721 [-1, -1, -1, -1, 0, -1, -1, -2, -1, -1] +cancelling -0.8 0.74833 [0, 0, -1, 0, -1, -1, 0, -2, -1, -2] +cancels -0.9 0.53852 [0, -1, 0, -1, -1, -1, -1, -1, -2, -1] +cancer -3.4 0.8 [-2, -4, -3, -4, -4, -2, -3, -4, -4, -4] +capable 1.6 0.4899 [1, 2, 2, 2, 1, 1, 2, 2, 1, 2] +captivated 1.6 0.4899 [2, 1, 2, 2, 2, 2, 1, 2, 1, 1] +care 2.2 0.74833 [2, 1, 2, 1, 2, 2, 3, 3, 3, 3] +cared 1.8 0.74833 [1, 2, 1, 3, 1, 2, 2, 2, 3, 1] +carefree 1.7 0.64031 [1, 1, 2, 2, 2, 2, 1, 1, 2, 3] +careful 0.6 1.11355 [3, 0, 0, 0, 1, 0, -1, 2, 1, 0] +carefully 0.5 0.67082 [1, 0, 1, 1, 0, 1, 0, 1, 1, -1] +carefulness 2.0 0.44721 [3, 2, 2, 1, 2, 2, 2, 2, 2, 2] +careless -1.5 0.5 [-2, -1, -1, -2, -2, -1, -2, -2, -1, -1] +carelessly -1.0 0.44721 [-1, -1, -1, -2, -1, 0, -1, -1, -1, -1] +carelessness -1.4 0.4899 [-2, -1, -1, -2, -1, -2, -1, -1, -2, -1] +carelessnesses -1.6 1.11355 [-4, -2, -2, -3, -1, 0, -1, -1, -1, -1] +cares 2.0 0.7746 [2, 3, 1, 3, 1, 2, 2, 2, 3, 1] +caring 2.2 0.4 [2, 3, 2, 2, 2, 2, 2, 3, 2, 2] +casual 0.8 0.74833 [1, 1, 0, 1, 0, 2, 0, 1, 2, 0] +casually 0.7 1.00499 [1, 0, 0, 0, 1, 0, 0, 3, 2, 0] +casualty -2.4 0.91652 [-4, -3, -3, -2, -1, -2, -3, -1, -2, -3] +catastrophe -3.4 0.4899 [-3, -3, -3, -4, -4, -3, -3, -3, -4, -4] +catastrophic -2.2 2.22711 [-3, -2, -4, -4, -4, -3, -2, -4, 2, 2] +cautious -0.4 0.66332 [0, 1, -1, 0, 0, 0, -1, -1, -1, -1] +celebrate 2.7 1.00499 [4, 4, 3, 2, 4, 2, 2, 2, 3, 1] +celebrated 2.7 0.78102 [2, 3, 3, 2, 3, 4, 3, 3, 1, 3] +celebrates 2.7 0.64031 [2, 3, 3, 2, 2, 3, 3, 3, 4, 2] +celebrating 2.7 0.64031 [3, 3, 4, 2, 2, 2, 3, 3, 2, 3] +censor -2.0 1.34164 [0, -3, -2, -3, -3, 0, -4, -1, -1, -3] +censored -0.6 1.68523 [-1, -1, -1, 2, -3, -2, -1, -1, -1, 3] +censors -1.2 1.07703 [-1, 0, -3, 0, -1, 0, -1, -2, -1, -3] +certain 1.1 0.7 [1, 0, 2, 0, 2, 2, 1, 1, 1, 1] +certainly 1.4 1.0198 [3, 2, 0, 1, 3, 1, 0, 1, 1, 2] +certainties 0.9 1.44568 [0, -2, 4, 0, 1, 1, 1, 1, 2, 1] +certainty 1.0 0.89443 [2, 1, 0, 1, 0, 0, 2, 2, 2, 0] +chagrin -1.9 0.53852 [-1, -2, -3, -2, -1, -2, -2, -2, -2, -2] +chagrined -1.4 1.2 [-1, -2, 2, -1, -2, -2, -2, -2, -2, -2] +challenge 0.3 1.00499 [1, 0, -1, 1, 1, -1, 1, 0, 2, -1] +challenged -0.4 1.62481 [0, -2, 1, -1, -3, -1, 3, -1, 1, -1] +challenger 0.5 1.43178 [0, 0, 2, -1, -2, 1, 3, 0, 2, 0] +challengers 0.4 1.56205 [0, -2, -1, 1, 1, 2, 3, 2, -1, -1] +challenges 0.3 1.48661 [0, -1, 2, -1, -2, 0, 3, 0, 2, 0] +challenging 0.6 0.91652 [0, 0, 0, 1, 1, -1, 0, 2, 2, 1] +challengingly -0.6 1.68523 [0, -1, -2, 1, -3, 2, -2, -1, 2, -2] +champ 2.1 0.83066 [2, 2, 2, 3, 2, 3, 2, 0, 3, 2] +champac -0.2 0.6 [0, 0, -2, 0, 0, 0, 0, 0, 0, 0] +champagne 1.2 1.07703 [1, 2, 2, 3, 0, 2, 0, 0, 2, 0] +champagnes 0.5 0.92195 [0, 0, 0, 0, 0, 1, 1, 3, 0, 0] +champaign 0.2 0.6 [0, 0, 0, 0, 2, 0, 0, 0, 0, 0] +champaigns 0.5 0.67082 [1, 0, 0, 0, 0, 0, 0, 1, 2, 1] +champaks -0.2 0.6 [0, 0, 0, 0, 0, 0, -2, 0, 0, 0] +champed 1.0 0.63246 [1, 1, 2, 1, 1, 2, 1, 0, 0, 1] +champer -0.1 0.53852 [0, -1, 1, 0, 0, 0, 0, -1, 0, 0] +champers 0.5 0.67082 [1, 0, 0, 0, 0, 0, 0, 1, 1, 2] +champerties -0.1 0.83066 [0, -1, 1, 1, 0, 0, 0, 0, -2, 0] +champertous 0.3 0.78102 [0, 0, 0, 1, -1, 2, 1, 0, 0, 0] +champerty -0.2 1.32665 [-2, -1, 0, -1, 0, 0, 0, -2, 2, 2] +champignon 0.4 0.8 [0, 0, 0, 0, 0, 2, 0, 2, 0, 0] +champignons 0.2 0.6 [0, 2, 0, 0, 0, 0, 0, 0, 0, 0] +champing 0.7 1.34536 [0, 2, 0, 3, 1, 1, 2, 0, -2, 0] +champion 2.9 0.83066 [3, 2, 3, 4, 4, 3, 2, 2, 4, 2] +championed 1.2 1.53623 [2, 1, 3, 1, 1, -3, 2, 2, 1, 2] +championing 1.8 0.9798 [1, 3, 2, 0, 3, 1, 2, 2, 1, 3] +champions 2.4 1.42829 [4, 0, 0, 3, 1, 3, 4, 3, 3, 3] +championship 1.9 1.04403 [3, 1, 1, 3, 2, 1, 3, 3, 0, 2] +championships 2.2 0.74833 [2, 2, 1, 2, 3, 2, 4, 2, 2, 2] +champs 1.8 0.4 [2, 2, 2, 2, 1, 2, 1, 2, 2, 2] +champy 1.0 1.0 [3, 0, 0, 0, 0, 2, 1, 2, 1, 1] +chance 1.0 0.7746 [1, 1, 0, 0, 0, 2, 1, 2, 1, 2] +chances 0.8 0.4 [0, 1, 1, 0, 1, 1, 1, 1, 1, 1] +chaos -2.7 0.9 [-2, -2, -3, -1, -4, -3, -3, -2, -3, -4] +chaotic -2.2 1.4 [-3, -2, -1, -2, -3, 1, -2, -2, -4, -4] +charged -0.8 0.87178 [-1, -2, -2, -1, -1, 0, -1, 1, 0, -1] +charges -1.1 0.7 [-2, -2, -2, -1, -1, 0, -1, -1, 0, -1] +charitable 1.7 0.64031 [1, 2, 1, 2, 2, 1, 2, 1, 3, 2] +charitableness 1.9 0.9434 [3, 1, 1, 3, 1, 3, 3, 2, 1, 1] +charitablenesses 1.6 1.74356 [2, 2, 3, 4, 1, -1, -2, 3, 2, 2] +charitably 1.4 0.66332 [1, 2, 1, 2, 2, 1, 0, 1, 2, 2] +charities 2.2 0.6 [3, 3, 2, 2, 1, 2, 2, 3, 2, 2] +charity 1.8 0.87178 [1, 3, 2, 2, 2, 1, 2, 0, 2, 3] +charm 1.7 0.78102 [3, 1, 1, 3, 2, 2, 1, 1, 1, 2] +charmed 2.0 0.63246 [3, 1, 2, 2, 2, 3, 1, 2, 2, 2] +charmer 1.9 0.53852 [3, 2, 2, 2, 2, 2, 1, 1, 2, 2] +charmers 2.1 0.83066 [2, 1, 2, 2, 4, 3, 2, 1, 2, 2] +charmeuse 0.3 0.78102 [0, 0, 0, 1, 0, 2, 1, 0, -1, 0] +charmeuses 0.4 0.66332 [0, 0, 1, 0, 1, 0, 0, 0, 0, 2] +charming 2.8 0.4 [3, 3, 3, 3, 3, 3, 2, 3, 2, 3] +charminger 1.5 0.67082 [2, 3, 1, 2, 1, 1, 2, 1, 1, 1] +charmingest 2.4 0.66332 [2, 3, 3, 1, 3, 2, 3, 3, 2, 2] 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2] +cheerful 2.5 0.67082 [3, 2, 3, 2, 2, 2, 4, 2, 3, 2] +cheerfuller 1.9 0.83066 [3, 3, 2, 3, 2, 1, 1, 2, 1, 1] +cheerfullest 3.2 0.87178 [4, 4, 4, 4, 3, 2, 2, 3, 2, 4] +cheerfully 2.1 0.83066 [3, 2, 2, 2, 1, 3, 1, 3, 1, 3] +cheerfulness 2.1 0.9434 [3, 2, 1, 2, 3, 4, 1, 2, 1, 2] +cheerier 2.6 0.4899 [2, 2, 3, 3, 2, 3, 3, 2, 3, 3] +cheeriest 2.2 0.6 [3, 2, 3, 1, 2, 2, 3, 2, 2, 2] +cheerily 2.5 0.67082 [3, 3, 2, 3, 2, 4, 2, 2, 2, 2] +cheeriness 2.5 0.67082 [3, 2, 4, 2, 3, 2, 3, 2, 2, 2] +cheering 2.3 0.64031 [3, 3, 2, 1, 3, 2, 2, 2, 3, 2] +cheerio 1.2 0.6 [2, 1, 1, 1, 2, 1, 1, 1, 2, 0] +cheerlead 1.7 0.78102 [1, 2, 0, 2, 2, 2, 2, 3, 1, 2] +cheerleader 0.9 0.9434 [1, 1, 0, 2, 1, 0, 0, 1, 0, 3] +cheerleaders 1.2 1.07703 [2, 0, 0, 1, 1, 0, 3, 3, 1, 1] +cheerleading 1.2 1.07703 [2, 2, 0, 0, 1, 0, 3, 2, 0, 2] +cheerleads 1.2 1.07703 [2, 3, 0, 3, 1, 0, 0, 1, 1, 1] +cheerled 1.5 1.11803 [0, 2, 1, 4, 2, 2, 2, 1, 1, 0] +cheerless -1.7 1.1 [-2, -3, -2, -2, -3, -2, -1, -1, 1, -2] +cheerlessly -0.8 1.98997 [-2, 4, -1, -2, -1, -2, -2, -2, 2, -2] +cheerlessness -1.7 1.48661 [-2, -1, -2, -3, -2, -4, -1, 2, -2, -2] +cheerly 2.4 0.66332 [2, 2, 3, 2, 2, 3, 4, 2, 2, 2] +cheers 2.1 1.3 [2, 2, 1, 3, 2, 3, 3, 4, -1, 2] +cheery 2.6 0.66332 [3, 2, 2, 3, 2, 3, 4, 2, 3, 2] +cherish 1.6 1.49666 [0, 3, 3, 3, 2, 2, 2, 1, -2, 2] +cherishable 2.0 1.41421 [-2, 2, 2, 2, 3, 2, 3, 3, 2, 3] +cherished 2.3 0.64031 [3, 2, 2, 3, 2, 2, 1, 3, 2, 3] +cherisher 2.2 0.4 [2, 2, 3, 2, 2, 2, 2, 3, 2, 2] +cherishers 1.9 0.7 [3, 3, 2, 2, 1, 1, 2, 2, 2, 1] +cherishes 2.2 0.74833 [2, 2, 3, 2, 2, 2, 2, 4, 2, 1] +cherishing 2.0 0.7746 [3, 3, 2, 2, 1, 2, 1, 3, 2, 1] +chic 1.1 1.3 [1, 2, 2, -2, 2, 0, 1, 1, 3, 1] +childish -1.2 0.74833 [-1, -1, -2, -3, -1, 0, -1, -1, -1, -1] +chilling -0.1 1.92094 [3, -2, 0, 1, -2, -2, -1, -2, 1, 3] +choke -2.5 0.92195 [-1, -2, -3, -3, -2, -4, -2, -4, -2, -2] +choked -2.1 1.3 [-4, -3, 0, -2, -1, -3, -3, -2, 0, -3] +chokes -2.0 0.89443 [-4, -3, -1, -2, -1, -2, -2, -2, -1, -2] +choking -2.0 1.26491 [-4, -2, -2, -3, -2, -2, -3, -1, 1, -2] +chuckle 1.7 0.45826 [2, 1, 2, 2, 2, 2, 1, 1, 2, 2] +chuckled 1.2 0.9798 [2, 2, 1, 1, 2, 0, 1, 2, -1, 2] +chucklehead -1.9 0.53852 [-2, -2, -1, -3, -2, -2, -2, -2, -1, -2] +chuckleheaded -1.3 1.84662 [-3, -4, -2, 0, 3, -1, -2, 0, -2, -2] +chuckleheads -1.1 0.9434 [-1, -2, 0, -1, -1, -3, 0, 0, -2, -1] +chuckler 0.8 1.07703 [2, 1, -1, 0, 2, 1, 1, 2, -1, 1] +chucklers 1.2 0.87178 [1, 1, 2, 3, 1, 0, 1, 0, 2, 1] +chuckles 1.1 1.13578 [2, 2, -1, 1, 2, 1, 1, 2, -1, 2] +chucklesome 1.1 0.53852 [1, 1, 2, 1, 1, 1, 0, 2, 1, 1] +chuckling 1.4 0.4899 [1, 2, 1, 2, 1, 1, 2, 2, 1, 1] +chucklingly 1.2 0.4 [1, 1, 1, 1, 2, 1, 1, 1, 2, 1] +clarifies 0.9 1.13578 [-2, 1, 0, 2, 1, 2, 2, 1, 1, 1] +clarity 1.7 0.78102 [2, 1, 2, 3, 3, 1, 1, 2, 1, 1] +classy 1.9 0.53852 [1, 2, 2, 1, 3, 2, 2, 2, 2, 2] +clean 1.7 0.78102 [3, 1, 2, 1, 2, 1, 3, 2, 1, 1] +cleaner 0.7 0.78102 [1, 0, 1, 0, 0, 0, 2, 1, 2, 0] +clear 1.6 1.2 [2, 1, 1, 0, 3, 1, 2, 4, 2, 0] +cleared 0.4 0.4899 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2, 2, 2, 1, 1, 2] +easier 1.8 0.9798 [1, 1, 2, 2, 4, 3, 2, 1, 1, 1] +easiest 1.8 1.07703 [2, 4, 1, 3, 2, 0, 2, 2, 1, 1] +easily 1.4 0.4899 [2, 1, 1, 2, 1, 2, 1, 2, 1, 1] +easiness 1.6 0.66332 [2, 1, 1, 2, 3, 2, 1, 1, 2, 1] +easing 1.0 0.63246 [0, 0, 2, 1, 1, 2, 1, 1, 1, 1] +easy 1.9 1.04403 [1, 4, 2, 1, 1, 3, 1, 3, 2, 1] +easygoing 1.3 0.45826 [1, 1, 1, 1, 1, 1, 2, 2, 2, 1] +easygoingness 1.5 0.67082 [1, 2, 1, 2, 1, 3, 1, 2, 1, 1] +ecstacy 3.3 1.18743 [4, 4, 3, 4, 4, 0, 3, 3, 4, 4] +ecstasies 2.3 1.34536 [3, 3, 2, 4, 3, 1, 3, -1, 2, 3] +ecstasy 2.9 1.75784 [4, 3, 3, 4, 4, 2, -2, 3, 4, 4] +ecstatic 2.3 1.34536 [3, 4, 3, 4, 1, 1, 1, 4, 1, 1] +ecstatically 2.8 1.93907 [3, 4, -1, 4, 4, 4, -1, 3, 4, 4] +ecstatics 2.9 0.83066 [1, 3, 4, 4, 3, 3, 3, 2, 3, 3] +eerie -1.5 0.67082 [-1, -1, -2, -2, -1, -1, -2, -1, -3, -1] +eery -0.9 1.04403 [-3, -1, -2, -1, -2, 0, 0, 0, 0, 0] +effective 2.1 0.83066 [2, 2, 2, 1, 3, 4, 2, 1, 2, 2] +effectively 1.9 0.7 [1, 2, 1, 2, 2, 1, 3, 3, 2, 2] +efficiencies 1.6 0.4899 [2, 1, 1, 2, 2, 2, 1, 2, 2, 1] +efficiency 1.5 0.5 [2, 1, 2, 2, 1, 2, 1, 1, 2, 1] +efficient 1.8 0.9798 [1, 2, 1, 1, 2, 3, 3, 0, 2, 3] +efficiently 1.7 0.78102 [1, 3, 2, 1, 3, 1, 1, 2, 2, 1] +effin -2.3 1.18743 [0, -3, -3, -3, -2, -1, -4, -1, -3, -3] +egotism -1.4 0.91652 [-2, -3, -1, -2, -2, 0, 0, -1, -2, -1] +egotisms -1.0 0.7746 [-1, -1, -1, -1, -1, 0, 0, -1, -3, -1] +egotist -2.3 0.9 [-2, -1, -2, -3, -4, -2, -3, -3, -1, -2] +egotistic -1.4 1.0198 [-2, -1, -1, -1, -2, 1, -3, -2, -1, -2] +egotistical -0.9 1.57797 [-1, -2, -2, -1, -2, 1, -3, 2, 1, -2] +egotistically -1.8 0.87178 [-2, -1, -1, -2, -1, -3, -3, -1, -1, -3] +egotists -1.7 0.78102 [-1, -2, 0, -2, -2, -2, -3, -1, -2, -2] +elated 3.2 0.74833 [2, 4, 4, 3, 4, 3, 3, 2, 3, 4] +elation 1.5 1.43178 [1, 2, -2, 2, 2, 3, 0, 3, 2, 2] +elegance 2.1 0.53852 [3, 2, 2, 1, 2, 2, 3, 2, 2, 2] +elegances 1.8 0.6 [2, 2, 1, 1, 2, 2, 2, 3, 2, 1] +elegancies 1.6 1.0198 [2, 1, 2, 1, 1, 0, 4, 1, 2, 2] +elegancy 2.1 0.53852 [3, 2, 2, 1, 2, 2, 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2] +emptied -0.7 0.64031 [-1, 0, 0, 0, -1, -1, -1, -2, 0, -1] +emptier -0.7 0.64031 [-1, 0, 0, 0, -1, -1, -1, -2, 0, -1] +emptiers -0.7 0.78102 [0, 0, -1, 0, -1, -1, -2, 0, -2, 0] +empties -0.7 0.64031 [-1, 0, 0, 0, -1, -1, -1, -2, 0, -1] +emptiest -1.8 1.07703 [-1, -2, -2, -1, -1, -2, -3, 0, -4, -2] +emptily -1.0 1.41421 [-2, 2, -1, -1, -1, 0, -4, -1, -1, -1] +emptiness -1.9 0.7 [-2, -2, -3, -2, -2, -1, -1, -3, -1, -2] +emptinesses -1.5 1.11803 [-1, -1, -3, -1, -1, -1, -2, 0, -4, -1] +emptins -0.3 0.45826 [0, 0, 0, -1, 0, 0, 0, -1, -1, 0] +empty -0.8 0.74833 [-1, -1, -1, -1, -1, -1, -2, 0, -1, 1] +emptying -0.6 1.0198 [2, -1, -1, 0, -1, 0, -1, -1, -2, -1] +enchanted 1.6 0.8 [1, 3, 1, 2, 1, 3, 1, 2, 1, 1] +encourage 2.3 0.78102 [2, 1, 3, 2, 3, 4, 2, 2, 2, 2] +encouraged 1.5 0.5 [1, 2, 2, 2, 2, 2, 1, 1, 1, 1] +encouragement 1.8 0.9798 [2, 1, 2, 1, 1, 3, 1, 2, 4, 1] +encouragements 2.1 0.7 [3, 2, 3, 2, 1, 2, 3, 2, 2, 1] +encourager 1.5 0.5 [2, 1, 2, 1, 1, 2, 1, 1, 2, 2] +encouragers 1.5 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0.74833 [-4, -2, -3, -2, -2, -3, -3, -2, -4, -3] +enrapture 3.0 0.63246 [2, 4, 3, 3, 4, 3, 3, 2, 3, 3] +enslave -3.1 0.9434 [-3, -4, -2, -4, -4, -2, -4, -2, -4, -2] +enslaved -1.7 2.41039 [3, -3, -3, -3, -4, -4, -4, 1, -1, 1] +enslaves -1.6 2.15407 [2, -2, -3, -2, -4, -4, -4, 1, -1, 1] +ensure 1.6 0.91652 [2, 1, 3, 1, 1, 2, 3, 2, 0, 1] +ensuring 1.1 0.9434 [0, 1, 3, 1, 1, 2, 1, 0, 2, 0] +enterprising 2.3 0.78102 [3, 2, 1, 3, 3, 2, 1, 2, 3, 3] +entertain 1.3 0.64031 [1, 2, 1, 1, 2, 1, 2, 0, 1, 2] +entertained 1.7 0.64031 [1, 2, 2, 1, 2, 1, 1, 3, 2, 2] +entertainer 1.6 1.2 [1, 4, 2, 2, 0, 0, 1, 3, 1, 2] +entertainers 1.0 0.7746 [0, 1, 2, 2, 0, 0, 1, 1, 2, 1] +entertaining 1.9 0.83066 [1, 2, 1, 1, 3, 2, 3, 2, 3, 1] +entertainingly 1.9 0.53852 [2, 1, 2, 3, 2, 2, 1, 2, 2, 2] +entertainment 1.8 0.9798 [2, 0, 4, 2, 2, 1, 2, 2, 1, 2] +entertainments 2.3 1.18743 [3, 3, 3, 2, 1, 0, 3, 4, 1, 3] +entertains 2.4 0.66332 [2, 2, 2, 2, 2, 3, 4, 3, 2, 2] +enthral 0.4 1.42829 [2, 2, 0, 2, 0, -1, -2, 2, 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3] +heroical 2.9 1.04403 [4, 4, 2, 4, 2, 3, 1, 4, 2, 3] +heroically 2.4 0.8 [2, 2, 2, 3, 3, 3, 4, 1, 2, 2] +heroicomic 1.0 1.0 [1, 0, 1, 0, 2, 0, 3, 1, 2, 0] +heroicomical 1.1 0.83066 [2, 0, 0, 2, 1, 2, 1, 2, 1, 0] +heroics 2.4 0.8 [2, 1, 2, 2, 2, 3, 3, 3, 4, 2] +heroin -2.2 1.83303 [0, -2, -4, 2, -4, -2, -2, -3, -3, -4] +heroine 2.7 1.1 [0, 2, 4, 4, 3, 3, 3, 3, 2, 3] +heroines 1.8 1.32665 [2, 1, 1, 4, 3, 1, 0, 3, 3, 0] +heroinism -2.0 2.0 [-3, -4, 2, -2, -4, -3, -2, -1, 1, -4] +heroism 2.8 0.6 [3, 3, 4, 3, 2, 2, 3, 3, 2, 3] +heroisms 2.2 0.87178 [3, 1, 2, 4, 3, 2, 2, 2, 1, 2] +heroize 2.1 0.7 [3, 2, 3, 1, 2, 2, 2, 3, 1, 2] +heroized 2.0 1.18322 [1, 0, 3, 3, 2, 0, 3, 3, 2, 3] +heroizes 2.2 0.9798 [1, 3, 2, 3, 4, 3, 2, 1, 2, 1] +heroizing 1.9 1.64012 [2, 3, -2, 2, 4, 3, 2, 2, 3, 0] +heron 0.1 0.3 [0, 0, 0, 0, 0, 1, 0, 0, 0, 0] +heronries 0.7 1.1 [2, 0, 0, 0, 2, 0, 3, 0, 0, 0] +heronry 0.1 0.9434 [0, 0, 0, 0, 0, 2, 0, 1, -2, 0] +herons 0.5 1.0247 [0, 0, 0, 3, 0, 2, 0, 0, 0, 0] +heros 1.3 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2, 2, 2, 1, 2, -1] +peaceably 2.0 0.63246 [2, 3, 2, 2, 1, 2, 3, 1, 2, 2] +peaceful 2.2 0.74833 [4, 2, 1, 2, 2, 2, 3, 2, 2, 2] +peacefuller 1.9 0.7 [2, 2, 2, 1, 3, 3, 2, 2, 1, 1] +peacefullest 3.1 0.7 [3, 3, 2, 3, 4, 2, 4, 3, 4, 3] +peacefully 2.4 0.66332 [3, 2, 2, 2, 4, 2, 2, 3, 2, 2] +peacefulness 2.1 0.83066 [3, 2, 1, 2, 3, 1, 3, 3, 1, 2] +peacekeeper 1.6 1.11355 [1, 1, 0, 2, 1, 1, 4, 3, 2, 1] +peacekeepers 1.6 1.11355 [4, 1, 1, 2, 1, 1, 2, 3, 0, 1] +peacekeeping 2.0 0.63246 [2, 1, 1, 2, 3, 3, 2, 2, 2, 2] +peacekeepings 1.6 0.8 [0, 1, 1, 2, 2, 3, 2, 2, 2, 1] +peacemaker 2.0 0.89443 [2, 1, 2, 4, 2, 2, 1, 3, 1, 2] +peacemakers 2.4 1.0198 [0, 3, 4, 2, 3, 2, 3, 3, 2, 2] +peacemaking 1.7 0.78102 [1, 1, 1, 3, 3, 1, 2, 2, 2, 1] +peacenik 0.8 0.87178 [1, 1, 0, 1, 2, 0, 1, 1, -1, 2] +peaceniks 0.7 1.00499 [2, 0, 0, 1, 0, 2, 1, -1, 2, 0] +peaces 2.1 0.83066 [2, 2, 2, 2, 3, 0, 3, 3, 2, 2] +peacetime 2.2 1.16619 [3, 1, 4, 2, 4, 3, 1, 1, 2, 1] +peacetimes 2.1 0.83066 [3, 2, 2, 4, 1, 2, 2, 2, 1, 2] +peculiar 0.6 1.2 [-1, 0, -1, 1, 2, -1, 2, 1, 2, 1] +peculiarities 0.1 1.37477 [-1, -1, 0, -1, -1, 1, 3, 2, -1, 0] +peculiarity 0.6 1.2 [-1, 1, -1, 0, 2, -1, 1, 2, 2, 1] +peculiarly -0.4 1.2 [-1, 2, -2, -1, 0, 0, -2, 1, -1, 0] +penalty -2.0 0.63246 [-2, -3, -2, -2, -1, -2, -3, -2, -2, -1] +pensive 0.3 1.1 [1, 0, 0, 1, 0, -1, 3, 0, -1, 0] +perfect 2.7 0.78102 [2, 4, 2, 3, 4, 2, 3, 2, 3, 2] +perfecta 1.4 1.42829 [1, 0, 0, 3, 1, 0, 0, 4, 3, 2] +perfectas 0.6 1.11355 [0, 0, -1, 1, 0, 2, 3, 0, 1, 0] +perfected 2.7 0.78102 [1, 3, 3, 4, 2, 3, 3, 2, 3, 3] +perfecter 1.8 0.9798 [2, 1, 3, 1, 2, 1, 2, 4, 1, 1] +perfecters 1.4 1.11355 [2, 1, 3, 0, 0, 3, 2, 0, 1, 2] +perfectest 3.1 1.04403 [2, 4, 4, 4, 3, 2, 1, 3, 4, 4] +perfectibilities 2.1 1.04403 [3, 2, 2, 3, 4, 1, 2, 2, 0, 2] +perfectibility 1.8 1.249 [4, 3, 3, 0, 1, 0, 1, 2, 2, 2] +perfectible 1.5 0.67082 [1, 2, 1, 1, 2, 1, 1, 3, 2, 1] +perfecting 2.3 0.9 [1, 2, 3, 3, 1, 2, 2, 4, 2, 3] +perfection 2.7 1.1 [3, 3, 3, 1, 2, 4, 4, 1, 2, 4] 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1.26491 [-2, -2, 0, -2, 0, -2, -2, -1, -1, 2] +rebelling -1.1 1.51327 [-3, -1, -2, -1, -2, -1, -1, -3, 2, 1] +rebellion -0.5 1.80278 [-2, -2, -1, -1, 3, -1, 1, -3, 2, -1] +rebellions -1.1 1.57797 [-2, -4, -3, 0, 0, -1, 1, -2, -1, 1] +rebellious -1.2 1.249 [-2, -1, -3, -1, -2, -2, 1, -1, 1, -2] +rebelliously -1.8 0.87178 [-3, 0, -2, -2, -1, -2, -3, -1, -2, -2] +rebelliousness -1.2 1.16619 [0, -3, -1, -2, 1, -1, -2, 0, -2, -2] +rebels -0.8 1.07703 [-1, 0, 0, -2, 0, -3, 0, 0, 0, -2] +recession -1.8 1.07703 [-3, -1, -4, -2, -1, -1, -1, -1, -3, -1] +reckless -1.7 0.64031 [-2, -1, -1, -3, -1, -2, -1, -2, -2, -2] +recommend 1.5 0.67082 [1, 1, 1, 1, 2, 3, 2, 2, 1, 1] +recommended 0.8 1.07703 [1, 1, 0, -2, 1, 2, 1, 2, 1, 1] +recommends 0.9 0.9434 [1, 1, 2, 0, 0, 2, 1, -1, 2, 1] +redeemed 1.3 0.9 [2, 1, 2, 2, 1, -1, 2, 1, 2, 1] +reek -2.4 0.66332 [-3, -3, -2, -3, -3, -2, -2, -2, -1, -3] +reeked -2.0 1.09545 [-4, -3, -2, -3, -1, -2, 0, -1, -2, -2] +reeker -1.7 1.1 [0, -2, -1, 0, -3, -3, -2, -1, -3, -2] +reekers -1.5 1.0247 [-3, 0, 0, 0, -2, -2, -2, -2, -2, -2] +reeking -2.0 1.48324 [2, -2, -2, -3, -3, -3, -1, -3, -3, -2] +refuse -1.2 0.4 [-1, -1, -1, -1, -1, -1, -1, -2, -2, -1] +refused -1.2 0.74833 [0, -1, -1, -1, -1, -1, -2, -1, -3, -1] +refusing -1.7 0.64031 [-1, -1, -1, -2, -2, -2, -3, -1, -2, -2] +regret -1.8 0.6 [-2, -2, -2, -2, -1, -3, -1, -1, -2, -2] +regretful -1.9 0.83066 [-1, -2, -1, -2, -1, -2, -1, -3, -3, -3] +regretfully -1.9 0.83066 [-1, -1, -1, -3, -2, -3, -1, -3, -2, -2] +regretfulness -1.6 0.66332 [-1, -3, -1, -1, -1, -2, -1, -2, -2, -2] +regrets -1.5 0.5 [-2, -2, -2, -1, -2, -1, -1, -2, -1, -1] +regrettable -2.3 0.78102 [-3, -1, -2, -1, -3, -2, -3, -3, -2, -3] +regrettably -2.0 0.63246 [-2, -3, -1, -3, -2, -1, -2, -2, -2, -2] +regretted -1.6 0.4899 [-2, -1, -2, -2, -2, -2, -1, -1, -1, -2] +regretter -1.6 0.66332 [-2, -1, -2, -2, -3, -2, -1, -1, -1, -1] +regretters -2.0 0.89443 [-1, -2, -2, -2, -4, -3, -1, -1, -2, -2] +regretting -1.7 0.78102 [-3, -2, -2, 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3, 3, 3, 2, 2] +relaxins 1.2 1.4 [1, 3, 2, 1, 0, 1, -2, 3, 2, 1] +relentless 0.2 1.07703 [3, -1, 0, -1, 0, 1, 0, 0, 0, 0] +reliant 0.5 1.20416 [0, 2, -1, 1, 0, 1, 2, -1, 2, -1] +relief 2.1 0.53852 [2, 2, 2, 3, 2, 3, 2, 1, 2, 2] +reliefs 1.3 0.78102 [1, 2, 2, 2, 2, 2, 0, 0, 1, 1] +relievable 1.1 1.22066 [1, -2, 1, 1, 1, 1, 3, 2, 1, 2] +relieve 1.5 0.5 [1, 2, 1, 2, 1, 1, 2, 2, 1, 2] +relieved 1.6 0.66332 [2, 1, 2, 1, 1, 3, 2, 1, 1, 2] +relievedly 1.4 0.4899 [1, 2, 1, 1, 1, 2, 2, 2, 1, 1] +reliever 1.5 0.80623 [2, 1, 2, 2, 1, 2, 0, 1, 1, 3] +relievers 1.0 0.63246 [1, 1, 1, 1, 2, 0, 2, 0, 1, 1] +relieves 1.5 0.80623 [2, 1, 2, 2, 1, 2, 0, 1, 1, 3] +relieving 1.5 1.0247 [2, 2, 1, 2, 3, -1, 2, 1, 1, 2] +relievo 1.3 1.00499 [0, 2, 1, 2, 2, -1, 2, 2, 2, 1] +relishing 1.6 0.8 [1, 2, 1, 3, 2, 3, 1, 1, 1, 1] +reluctance -1.4 0.4899 [-2, -2, -1, -1, -1, -1, -2, -1, -2, -1] +reluctancy -1.6 0.8 [-3, -2, -1, -1, -1, -1, -2, -1, -3, -1] +reluctant -1.0 0.7746 [0, -1, 0, -1, -1, 0, -1, -2, -2, -2] 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2, 3, 4, 2] +wonderfully 2.9 0.83066 [1, 3, 3, 4, 3, 2, 3, 3, 4, 3] +wonderfulness 2.9 0.53852 [3, 2, 3, 3, 3, 3, 3, 2, 4, 3] +woo 2.1 1.37477 [4, 2, 1, 3, 2, 2, -1, 2, 2, 4] +woohoo 2.3 1.1 [3, 3, 1, 4, 4, 2, 1, 1, 2, 2] +woot 1.8 1.07703 [2, 0, 2, 2, 2, 2, 0, 4, 2, 2] +worn -1.2 0.4 [-1, -1, -1, -1, -1, -1, -2, -1, -2, -1] +worried -1.2 0.74833 [-1, -1, -1, -1, -1, -2, -3, 0, -1, -1] +worriedly -2.0 0.44721 [-2, -2, -3, -2, -2, -2, -2, -1, -2, -2] +worrier -1.8 0.6 [-2, -2, -1, -2, -1, -3, -2, -2, -1, -2] +worriers -1.7 0.45826 [-2, -1, -2, -2, -2, -2, -1, -2, -1, -2] +worries -1.8 0.6 [-2, -2, -1, -2, -1, -2, -2, -3, -1, -2] +worriment -1.5 0.67082 [-1, -2, -1, -1, -1, -2, -1, -3, -1, -2] +worriments -1.9 0.7 [-2, -1, -2, -3, -1, -2, -3, -1, -2, -2] +worrisome -1.7 0.64031 [-1, -1, -1, -2, -1, -2, -3, -2, -2, -2] +worrisomely -2.0 0.63246 [-1, -2, -1, -2, -2, -3, -2, -2, -3, -2] +worrisomeness -1.9 0.53852 [-2, -2, -3, -1, -2, -2, -2, -1, -2, -2] +worrit -2.1 0.53852 [-2, -2, -1, -2, -2, -3, -3, -2, -2, -2] +worrits -1.2 0.9798 [-1, -2, -2, -1, 0, 0, -1, -3, 0, -2] +worry -1.9 0.7 [-2, -3, -1, -3, -1, -2, -1, -2, -2, -2] +worrying -1.4 0.66332 [-2, -1, -2, -2, -1, 0, -1, -1, -2, -2] +worrywart -1.8 0.9798 [-2, -2, -2, -1, -1, -1, -1, -3, -1, -4] +worrywarts -1.5 0.5 [-2, -1, -2, -2, -2, -1, -1, -1, -2, -1] +worse -2.1 0.83066 [-2, -2, -1, -3, -4, -2, -1, -2, -2, -2] +worsen -2.3 0.78102 [-4, -3, -1, -2, -2, -2, -2, -3, -2, -2] +worsened -1.9 1.22066 [-2, -2, -2, -1, -2, -2, -4, 1, -3, -2] +worsening -2.0 0.44721 [-2, -3, -2, -2, -2, -2, -1, -2, -2, -2] +worsens -2.1 0.53852 [-2, -2, -2, -2, -1, -2, -2, -3, -3, -2] +worser -2.0 0.89443 [-2, -2, -4, -1, -2, -2, -2, -3, -1, -1] +worship 1.2 1.07703 [1, 0, 0, 1, 3, 0, 2, 3, 1, 1] +worshiped 2.4 1.0198 [1, 2, 4, 3, 4, 1, 2, 3, 2, 2] +worshiper 1.0 1.0 [0, 0, 2, 3, 0, 2, 1, 1, 1, 0] +worshipers 0.9 0.83066 [0, 0, 0, 2, 1, 1, 1, 2, 2, 0] +worshipful 0.7 1.00499 [1, -1, 3, 1, 1, 1, 0, 0, 0, 1] +worshipfully 1.1 1.3 [0, 0, 0, 1, 3, 0, 3, 3, 1, 0] +worshipfulness 1.6 0.8 [3, 1, 2, 2, 1, 1, 3, 1, 1, 1] +worshiping 1.0 1.18322 [0, 3, 0, 3, 0, 1, 1, 2, 0, 0] +worshipless -0.6 1.0198 [0, -1, -3, -1, -1, -1, 0, 0, 0, 1] +worshipped 2.7 0.78102 [3, 2, 3, 3, 1, 4, 2, 3, 3, 3] +worshipper 0.6 0.66332 [1, 1, 0, 0, 1, 0, 0, 2, 1, 0] +worshippers 0.8 0.87178 [0, 1, 0, 0, 3, 1, 1, 1, 0, 1] +worshipping 1.6 1.28062 [1, 3, 3, 3, 0, 3, 1, 0, 2, 0] +worships 1.4 1.11355 [2, 0, 1, 3, 2, 1, 0, 3, 2, 0] +worst -3.1 1.04403 [-4, -4, -3, -1, -3, -4, -2, -2, -4, -4] +worth 0.9 0.9434 [0, 0, 1, 1, 2, 1, 1, 3, 0, 0] +worthless -1.9 1.13578 [-3, -1, -3, -4, -1, -3, -1, -1, -1, -1] +worthwhile 1.4 0.4899 [1, 1, 1, 2, 1, 1, 2, 1, 2, 2] +worthy 1.9 0.53852 [2, 2, 2, 1, 1, 2, 2, 2, 3, 2] +wow 2.8 0.9798 [2, 3, 2, 4, 4, 3, 3, 2, 1, 4] +wowed 2.6 0.8 [3, 3, 4, 3, 2, 1, 3, 3, 2, 2] +wowing 2.5 0.67082 [2, 2, 3, 3, 2, 3, 4, 2, 2, 2] +wows 2.0 1.61245 [2, 3, 3, 3, 2, 1, -2, 1, 4, 3] +wowser -1.1 2.02237 [-3, 3, 0, 2, -2, -1, -3, -2, -2, -3] +wowsers 1.0 2.14476 [0, -2, 4, 2, 3, 0, 1, 2, -3, 3] +wrathful -2.7 0.64031 [-3, -2, -2, -3, -3, -2, -4, -2, -3, -3] +wreck -1.9 0.7 [-1, -2, -3, -3, -2, -2, -2, -1, -1, -2] +wrong -2.1 1.04403 [-2, -2, -2, -2, -4, -4, -1, -1, -1, -2] +wronged -1.9 0.53852 [-2, -2, -2, -2, -2, -1, -3, -2, -2, -1] +x-d 2.6 0.91652 [2, 3, 3, 4, 1, 2, 3, 4, 2, 2] +x-p 1.7 0.45826 [2, 2, 1, 2, 2, 1, 1, 2, 2, 2] +xd 2.8 0.87178 [3, 3, 4, 2, 3, 3, 1, 2, 4, 3] +xp 1.6 0.4899 [2, 2, 2, 1, 1, 1, 2, 2, 1, 2] +yay 2.4 1.0198 [1, 3, 3, 2, 2, 1, 4, 4, 2, 2] +yeah 1.2 0.6 [1, 1, 1, 2, 1, 1, 0, 2, 1, 2] +yearning 0.5 1.0247 [0, 1, 0, 1, 0, 3, 0, 1, -1, 0] +yeees 1.7 1.00499 [1, 3, 1, 2, 1, 1, 4, 2, 1, 1] +yep 1.2 0.4 [1, 1, 1, 1, 1, 1, 2, 2, 1, 1] +yes 1.7 0.78102 [1, 2, 2, 1, 1, 1, 3, 3, 1, 2] +youthful 1.3 0.45826 [1, 2, 1, 2, 1, 1, 1, 1, 2, 1] +yucky -1.8 0.6 [-2, -1, -1, -2, -2, -1, -2, -2, -3, -2] +yummy 2.4 1.0198 [1, 2, 4, 3, 2, 2, 3, 1, 4, 2] +zealot -1.9 1.04403 [-2, -3, -1, -2, -1, -3, -4, -1, -1, -1] +zealots -0.8 1.83303 [-1, -2, -1, -2, -2, 1, -2, 4, -1, -2] +zealous 0.5 1.43178 [2, -1, 2, 1, 0, 0, 3, 0, -2, 0] +{: 1.8 0.9798 [1, 3, 2, 2, 1, 1, 4, 2, 1, 1] +|-0 -1.2 0.74833 [0, -2, -1, -1, -1, -1, -1, -1, -1, -3] +|-: -0.8 0.74833 [-1, -2, 0, -1, 0, -2, -1, -1, 0, 0] +|-:> -1.6 0.4899 [-1, -2, -2, -2, -2, -1, -1, -2, -2, -1] +|-o -1.2 0.9798 [-1, 0, -1, -1, -1, -1, -1, -4, -1, -1] +|: -0.5 1.68819 [2, -3, -1, 0, -1, -1, -1, -2, -1, 3] +|;-) 2.2 1.32665 [4, 1, 1, 1, 3, 2, 4, 1, 4, 1] +|= -0.4 1.56205 [2, -2, -1, 0, -1, -1, -1, -2, -1, 3] +|^: -1.1 0.7 [-2, 0, -1, -1, 0, -1, -1, -2, -2, -1] +|o: -0.9 0.53852 [-1, 0, -1, -2, -1, 0, -1, -1, -1, -1] +||-: -2.3 0.45826 [-2, -2, -2, -3, -3, -3, -2, -2, -2, -2] +}: -2.1 0.83066 [-1, -1, -3, -2, -3, -2, -2, -1, -3, -3] +}:( -2.0 0.63246 [-3, -1, -2, -1, -3, -2, -2, -2, -2, -2] +}:) 0.4 1.42829 [1, 1, -2, 1, 2, -2, 1, -1, 2, 1] +}:-( -2.1 0.7 [-2, -1, -2, -2, -2, -4, -2, -2, -2, -2] +}:-) 0.3 1.61555 [1, 1, -2, 1, -1, -3, 2, 2, 1, 1] \ No newline at end of file

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