Review Sentiment Prediction

This repository contains a Review Sentiment Analyzer that predicts the sentiment of text based on the Yelp dataset. The project is implemented using PyTorch and includes the following components:

• Vocabulary: A class for managing the mapping between tokens and their corresponding integer indices.
• ReviewVectorizer: A class for converting text reviews into numerical vectors.
• ReviewDataset: A class for handling the dataset and splitting it into training, validation, and test sets.
• ReviewClassifier: A PyTorch neural network model for sentiment classification.

Table of Contents

• Installation
• Usage
• Project Structure
• Contributing
• License

Installation

1. Clone the repository:

   git clone https://github.com/omartarekmoh/Review-Sentiment-Prediction.git
   cd Review-Sentiment-Prediction

2. Install the required dependencies:

   pip install -r requirements.txt

Usage

1. Data Preparation:

   Download the Yelp dataset from here and place it in the data/ directory -The one in the directory is a smaller version of the full dataset-.

2. Training the Model:

   Run the training script to train the model on the dataset:

   python train.py

3. Making Predictions:

   Use the trained model to predict the sentiment of new reviews:

   python test.py

Project Structure

• data/: Directory for storing the dataset.
• train.py: Script for training the model.
• test.py: Script for making predictions using the trained model.
• classes.py: Contains the core classes (Vocabulary, ReviewVectorizer, ReviewDataset).
• model.py: Contains the ReviewClassifier class.
• helpers.py: Contains utility functions for data processing and model training.

Contributing

Contributions are welcome! Please open an issue or submit a pull request if you have any improvements or bug fixes.

License

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