Emotion analysis is the process of recognizing and extracting emotions from text. This project aims to build a model for emotion analysis using Natural Language Processing (NLP) techniques.
- Python 3.x
- TensorFlow 2.x
- scikit-learn
- numpy
- pandas
The data used in this project is clik
The model is built using a TensorFlow 2.x deep learning model and is trained on a binary classification problem to predict the emotion of a given text. The model consists of an embedding layer, followed by several dense layers, and finally a softmax activation layer to output the probability of each emotion.
- Clone the repository: git clone https://github.com/agusabdulrahman/emotion-analysis-nlp.git
- Install the required packages: pip install -r requirements.txt
- Train the model: python train.py
- Test the model: python test.py
The model achieved an accuracy of xx% on the test data. Further improvement can be made by using more data, fine-tuning the model, and exploring different NLP techniques.
Contributions are welcome! If you want to contribute to this project, please create a fork, make your changes, and submit a pull request.