smuggler0p / nlp-sentiment-analysis

This repository hosts a collection of tools and models for sentiment analysis using Natural Language Processing (NLP) techniques. Sentiment analysis is the process of determining the emotional tone behind a piece of text, whether it's positive, negative, or neutral.

Key Features:

1. Preprocessing Utilities: Includes scripts and functions for text cleaning, tokenization, and normalization, crucial steps before performing sentiment analysis.
2. Lexicon-based Analysis: Implementation of lexicon-based sentiment analysis techniques such as Vader and TextBlob.
3. Machine Learning Models: Various machine learning models (e.g., Naive Bayes, Support Vector Machines, LSTM) for sentiment classification trained on different datasets.
4. Deep Learning Architectures: State-of-the-art deep learning architectures like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for sentiment analysis.
5. Evaluation Metrics: Functions to evaluate model performance using metrics like accuracy, precision, recall, and F1-score.
6. Interactive Web Interface: Integration with Flask or Streamlit to create a user-friendly web application for real-time sentiment analysis.

Usage:

• Researchers and practitioners can use this repository as a starting point for sentiment analysis projects, leveraging the provided utilities and models.
• Developers can fork this repository to extend its functionality or integrate it into their applications.
• Educational institutions can use it as a teaching resource for NLP and sentiment analysis courses.

Contributions: Contributions, bug reports, and feature requests are welcome. Please follow the contribution guidelines outlined in the repository's README.

License: This repository is licensed under the MIT License, granting users the freedom to use, modify, and distribute the code for both commercial and non-commercial purposes.

Acknowledgments: