Multi-Class Text Emotion Analysis

📌 Overview

This project is an open-source Multi-Class Text Emotion Analysis system that classifies text into different emotional categories. We use Count Vectorizer for feature extraction and Logistic Regression for classification.

🚀 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

• Python
• scikit-learn
• pandas
• 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

🔧 Installation

Clone this repository and install the required dependencies:

git clone https://github.com/AKing-283/multi-class-text-emotion-analysis.git
cd multi-class-emotion-analysis
pip install -r requirements.txt

Disclaimer

This model is trained using Logistic Regression and Count Vectorizer. When words below 20 is used in this then it will give wrong output so a word limit of 20 is needed in this for good output

🔗 Contributing

Feel free to open an issue or submit a pull request if you find any improvements or bugs.

📜 License

This project is licensed under the MIT License.

---

⭐ If you find this project helpful, consider giving it a star! ⭐