/SentimentModel

SentimentModel is a simple sentiment analysis tool that processes text data to determine the sentiment of user reviews or social media posts. The model predicts whether the sentiment of a given text is positive or negative.

Primary LanguagePythonMIT LicenseMIT

SENTIMENTMODEL

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Developed with the software and tools below.

Python

SentimentModel is a simple sentiment analysis tool that processes text data to determine the sentiment of user reviews or social media posts. The model predicts whether the sentiment of a given text is positive or negative.

Warning

This tool is not 100% accurate and should not be used for critical decision-making.

Features

  • Preprocesses text data by converting to lowercase and removing punctuation.
  • Uses a pre-trained machine learning model to predict sentiment.
  • Easy to use with a simple Python interface.

Getting Started

Requirements

Ensure you have the following dependencies installed on your system:

Installation

  1. Clone the SentimentModel repository:
git clone https://github.com/abhixdd/SentimentModel
  1. Change to the project directory:
cd SentimentModel
  1. Install the dependencies:
pip install -r requirements.txt

Running SentimentModel

Use the following command to run SentimentModel:

python main.py

Contributing

Contributions are welcome! Here are several ways you can contribute:

  • Submit Pull Requests: Review open PRs, and submit your own PRs.
  • Join the Discussions: Share your insights, provide feedback, or ask questions.
  • Report Issues: Submit bugs found or log feature requests for Sentimentmodel.
Contributing Guidelines
  1. Fork the Repository: Start by forking the project repository to your GitHub account.
  2. Clone Locally: Clone the forked repository to your local machine using a Git client. 
    git clone https://github.com/abhixdd/SentimentModel
  3. Create a New Branch: Always work on a new branch, giving it a descriptive name. 
    git checkout -b new-feature-x
  4. Make Your Changes: Develop and test your changes locally.
  5. Commit Your Changes: Commit with a clear message describing your updates. 
    git commit -m 'Implemented new feature x.'
  6. Push to GitHub: Push the changes to your forked repository. 
    git push origin new-feature-x
  7. Submit a Pull Request: Create a PR against the original project repository. Clearly describe the changes and their motivations.

Once your PR is reviewed and approved, it will be merged into the main branch.

License

This project is protected under the MIT License.