Sentiment Classifier for Twitter Data

This project processes Twitter data to classify tweets based on sentiment. Using Python, it calculates positive, negative, and net scores for each tweet based on predefined word lists. The script then outputs a CSV file with these scores, along with the number of retweets and replies for each tweet.

Key Features

Data Processing:

• Reads Twitter data from project_twitter_data.csv.
• Cleans tweets by removing punctuation for accurate word matching.

Sentiment Analysis:

• Uses positive_words.txt and negative_words.txt to identify sentiment scores for tweets.
• Outputs sentiment scores as:
  • Positive Score: Count of positive words.
  • Negative Score: Count of negative words.
  • Net Score: Positive score minus negative score.

Result Export:

• Outputs processed data to resulting_data.csv, including:
  • Number of retweets.
  • Number of replies.
  • Positive, negative, and net sentiment scores.

Input

project_twitter_data.csv:

• A CSV file containing columns for tweets, number of retweets, and replies.

Word Lists:

• positive_words.txt: A list of predefined positive words.
• negative_words.txt: A list of predefined negative words.

Output

resulting_data.csv:

• A CSV file containing:
  • Number of retweets.
  • Number of replies.
  • Positive score.
  • Negative score.
  • Net score.

How to Use

1. Clone the repository and place the required files (project_twitter_data.csv, positive_words.txt, and negative_words.txt) in the assets directory.
2. Run the script to process the data:

   python sentiment_classifier.py

3. View the results in the resulting_data.csv file.