This Python script performs sentiment analysis on textual data using the TextBlob library. It reads data from a CSV file, calculates sentiment scores for each entry, exports the results, and visualizes the distribution of sentiment scores as a bell curve.
Before you can run this script, you need to have Python installed on your system along with the following Python libraries:
- pandas
- numpy
- matplotlib
- textblob
You can install these libraries using pip:
$ pip install -r requirements.txt
The script expects a CSV file named "Overall_Sentiment_Data_All_Data.csv" by default. This file should contain a column labeled 'Column1.data.body' which holds the text to be analyzed for sentiment.
The script will generate a CSV file named "Sentiment_Analysis_Results.csv" containing the original data along with a new 'Sentiment' column that holds the sentiment score for each text. Scores range from -1 (very negative) to 1 (very positive), with 0 being neutral.
Additionally, a plot titled 'Sentiment Analysis Bell Curve' will be displayed, showing the distribution of sentiment scores across the dataset. The plot includes lines indicating the average, lowest, and highest sentiment scores.
To run the script, simply navigate to the directory containing the script and input data file, and execute the script using Python:
python main.py
You can customize the input and output CSV filenames by modifying the csv_file and output_csv variables in the script.
This script is released under the MIT License. Feel free to use it or modify it as per your needs.