Sentiment-Analysis

Sentiment-Analysis

This repository contains the code and resources for performing sentiment analysis using natural language processing techniques.

Overview

The sentiment analysis.ipynb file in this repository provides a step-by-step guide on how to perform sentiment analysis on textual data. It covers the following topics:

1. Data preprocessing: Cleaning and preparing the text data for analysis.
2. Feature extraction: Transforming the text data into numerical features.
3. Model training: Building and training a sentiment analysis model using machine learning algorithms.
4. Model evaluation: Assessing the performance of the trained model.
5. Sentiment analysis application: Applying the trained model to analyze the sentiment of new text data.

Dependencies

To run the code in this repository, you will need the following dependencies:

• Python 3.7 or higher
• Jupyter Notebook
• Pandas
• NumPy
• Scikit-learn
• NLTK (Natural Language Toolkit)

Usage

To use the code in this repository, follow these steps:

1. Clone the repository to your local machine.
2. Open the sentiment-analysis.ipynb file in Jupyter Notebook.
3. Install the required dependencies if not already installed.
4. Run the code cells in the notebook sequentially to perform sentiment analysis on your own text data.

Contributing

If you would like to contribute to this project, please follow these guidelines:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes and commit them with descriptive messages.
4. Push your changes to your forked repository.
5. Submit a pull request to the main repository.

License

This project is licensed under the MIT License. See the LICENSE file for more information.

Contact

If you have any questions or suggestions, feel free to contact.