Welcome to the Daily Fantasy Sports (DFS) Article Sentiment Analyzer! This program is designed to help you analyze daily fantasy sports articles by extracting named entities and their sentiment scores. To use the program, simply copy and paste your DFS article of choice into the dfs_test.txt file, and the program will generate a CSV file containing the named entities and their sentiment scores.
Make sure you have the following installed:
- Python 3.6 or higher
- Required Python packages:
- nltk
- pandas
- textract
Follow these simple steps to use the DFS Article Sentiment Analyzer:
- Clone or download this repository to your local machine.
- Locate the
dfs_test.txtfile within the repository. - Open the
dfs_test.txtfile with a text editor of your choice. - Copy and paste the content of your daily fantasy sports article into the
dfs_test.txtfile. - Save the
dfs_test.txtfile. - The first time you run the program, ensure that the
nltk.downloadlines are uncommented in thedfs_article_sentiment_analyzer.pyscript to download the necessary resources. You can comment these lines out during subsequent runs. - Run the main program:
python dfs_article_sentiment_analyzer.py
After running the program, you will find the analysis results in a newly generated CSV file named dfs_test3.csv. Open this file to view the extracted named entities and their sentiment scores.
The DFS Article Sentiment Analyzer includes the following features:
- Named Entity Recognition to extract player and team names
- Sentiment analysis for each unique entity
- Merging single-named entities with the same sentiment score
- Exporting results to a CSV file
We welcome contributions to improve the DFS Article Sentiment Analyzer. Please feel free to submit issues, feature requests, and pull requests.
This project is licensed under the MIT License - see the LICENSE file for details.