This project is a Python-based tool for processing and extracting information from text documents related to business management. The tool utilizes natural language processing (NLP) and machine learning techniques to analyze documents from the 1990s to 2022. The program can analyze individual documents, groups of documents from 5-year or 10-year intervals, or documents from specific years. The tool also generates tag clouds, cluster analysis, and top2vec results.
- Processes text documents related to business management
- Utilizes NLP and machine learning techniques
- Can analyze documents from 1990s to 2022
- Allows for analysis of individual documents, groups of documents from 5-year or 10-year intervals, or documents from specific years
- Generates tag clouds, cluster analysis with DBscan, and top2vec results
To use the tool, you will need to have Python and the necessary dependencies installed. Once you have the necessary components, you can run the program by executing the appropriate script. The program will prompt you for the necessary input, such as the location of the documents to be analyzed and the time frame for the analysis.
The tool generates a variety of results, including tag clouds, cluster analysis, and top2vec results. These results can be used to identify trends and patterns in the analyzed documents. Additionally, the results can be used to compare the content of the documents over time, which can be useful for identifying changes in business management practices.
This project provides a powerful tool for analyzing business management documents. The tool's ability to analyze documents from multiple time frames and generate a variety of results makes it a valuable tool for anyone interested in understanding trends and patterns in business management practices. So, it could be very helpful for managers, business analysts, and researchers.