Sentiment Analysis Project Overview This project is a simple sentiment analysis tool that uses a logistic regression model to classify text as positive, negative, or neutral. The project includes a graphical user interface (GUI) built with Tkinter that allows users to enter text, analyze its sentiment, and save or load text files.

Features Sentiment Analysis: Enter text into the GUI and click the "Analyze Sentiment" button to classify the text as positive, negative, or neutral. Save Text: Save the text in the GUI to a file using the "Save Text" button. Load Text: Load text from a file into the GUI using the "Load Text" button. Clear Text: Clear the text in the GUI and the result label using the "Clear Text" button. Requirements Python: This project requires Python 3.8 or later to run. Tkinter: This project uses Tkinter for the GUI, which is included with Python. NLTK: This project uses the Natural Language Toolkit (NLTK) for text preprocessing and tokenization. You can install NLTK using pip: pip install nltk Scikit-learn: This project uses scikit-learn for the logistic regression model. You can install scikit-learn using pip: pip install scikit-learn Installation Clone this repository using Git: bash Copy code git clone https://github.com/OSOSerious/sentiment-analysis Install the required libraries using pip: bash Copy code pip install nltk scikit-learn Download the NLTK data files using the following Python code: python Copy code import nltk nltk.download('punkt') nltk.download('stopwords') Usage Run the project using Python: bash Copy code python sentiment_analysis.py Enter text into the GUI and click the "Analyze Sentiment" button to classify the text. Use the "Save Text", "Load Text", and "Clear Text" buttons to manage text files and clear the GUI. License This project is licensed under the MIT License. See the LICENSE file for details.

Contributing Contributions are welcome! If you'd like to contribute to this project, please fork the repository and submit a pull request.

Acknowledgments This project uses the following libraries and resources:

NLTK: For text preprocessing and tokenization. Scikit-learn: For the logistic regression model.