Multi-Class Text Emotion Analysis

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Features

- Supports multiple emotion categories (e.g., joy, anger, sadness, etc.).

- Uses **Count Vectorizer** for text transformation.

- Implements **Logistic Regression** for classification.

- Tested using streamlit

- Open-source and easy to use.

Tech Stack

Prerequisites

- Python 3.x

- scikit-learn (`import scikit-learn`)

- pandas (`import pandas`)

- numpy (`import numpy`)

- Flask

-Natural Language Processing

📊 Dataset

The dataset consists of labeled text samples with different emotion categories. Ensure your dataset is in CSV format with columns:

- Dataset: https://www.kaggle.com/datasets/pashupatigupta/emotion-detection-from-text

- `text`: The input text.

- `emotion`: The corresponding emotion label wiith emotions as Anger,Sad,,Happy,Love,Neutral

Get Started

Installation

To contribute to the Multi-Class Text Emotion Analysis repository, follow these steps:

1. Fork the Repository: Click on the "Fork" button on the repository's GitHub page to create a copy of the repository in your GitHub account.

2. Clone the repository: Clone the forked repository to your local machine using the following command in your terminal.

    git clone https://github.com/AKing-283/Multi-class-Text-Emotion-Analysis
    cd Multi-class-Text-Emotion-Analysis

3. Install dependencies:

       pip install -r requirements.txt

4. Add a remote upstream:

   git remote add upstream Multi-class-Text-Emotion-Analysis

5. Create a new branch: Create a new branch for your changes. Run the following command in your terminal.

   git checkout -b <your-branch-name>

6. Make the desired changes: Make the desired changes to the source code.

7. Add your changes: Add your changes to the staging area. Run the following command in your terminal.

   git add <File1 changed> <File2 changed> ...

8. Commit your changes: Commit your changes with a meaningful commit message. Run the following command in your terminal.

   git commit -m "<your-commit-message>"

9. Push your changes: Push your changes to your forked repository. Run the following command in your terminal

   git push origin <your-branch-name>

10. Create a Pull Request: Go to the GitHub page of your forked repository. You should see a prompt to create a pull request (PR). Click on it, compare the changes, and create the PR.
    
    

📜 License

This project is licensed under the MIT License.

Project Admin:

Puspak Dakkata

Our Contributors

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