Senty.py: Sentiment Analysis Tool

Senty.py is a comprehensive sentiment analysis application built with Python, leveraging Streamlit for an interactive web interface. It uses a pre-trained Logistic Regression model for sentiment prediction, NRCLex for emotion analysis, and provides functionalities for text, batch, and audio analysis.

Features

• Text Analysis: Analyze the sentiment of a single text input.
• Batch Analysis: Process and analyze sentiment for multiple texts in a CSV file.
• Audio Analysis: Transcribe and analyze sentiment from an audio file.
• Sentiment Gauge: Visual representation of sentiment polarity.
• Emotion Distribution: Charts showing the intensity of different emotions in the text.
• Word Clouds: Generate word clouds for different sentiment categories.
• Model Information: Insights into the model's performance, including a confusion matrix and class distribution.

Installation

To run Senty.py locally, you need Python 3.6 or later. Clone the repository and install the required dependencies.

git clone https://github.com/haruki25/Sentiment_Analysis.git
cd Sentiment_Analysis
pip install -r requirements.txt

Dependencies

• Streamlit
• Pandas
• NLTK
• Scikit-learn
• Plotly
• Matplotlib
• Seaborn
• WordCloud
• NRCLex

Usage

After installing the dependencies, you can start the Streamlit application by running:

streamlit run App.py

Navigate to http://localhost:8501 in your web browser to interact with the application.

Structure

• App.py: The main Streamlit application file. (Homepage)
• utils/: Contains utility scripts for sentiment analysis, batch processing, and model training.
  • batch_processing.py: Functions for processing batches of text for sentiment analysis.
  • train_model.py: Scripts for training the sentiment analysis model.
  • utils.py: Various utility functions including sentiment gauge creation, word cloud generation, and more.
• model_info.py: Streamlit application for displaying model information and performance metrics.
• datasets/: Directory for storing training datasets (included in the repository).

Contributing

Contributions to Senty.py are welcome! Please follow these steps to contribute:

1. Fork the repository.
2. Create a new branch for your feature (git checkout -b feature/AmazingFeature).
3. Commit your changes (git commit -m 'Add some AmazingFeature').
4. Push to the branch (git push origin feature/AmazingFeature).
5. Open a pull request.

License

Distributed under the MIT License. See LICENSE for more information.

Acknowledgments

• NLTK for natural language processing tools.
• Scikit-learn for machine learning algorithms.
• Streamlit for creating an interactive web application.

Thank you for exploring Senty.py. Happy sentiment analyzing!