https://github.com/nssharmaofficial/review-sentiment-classifier
Review classification in pytorch using LSTM
https://github.com/nssharmaofficial/review-sentiment-classifier
classification embedding-layer embedding-vectors imdb-dataset lstm lstm-layer nlp pytorch
Last synced: 6 days ago
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Review classification in pytorch using LSTM
- Host: GitHub
- URL: https://github.com/nssharmaofficial/review-sentiment-classifier
- Owner: nssharmaofficial
- License: mit
- Created: 2023-01-01T12:40:59.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-09-06T06:22:55.000Z (8 months ago)
- Last Synced: 2025-03-29T22:06:02.386Z (30 days ago)
- Topics: classification, embedding-layer, embedding-vectors, imdb-dataset, lstm, lstm-layer, nlp, pytorch
- Language: Python
- Homepage:
- Size: 30.1 MB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Review sentiment classifier
The sentiment classifier uses a Long Short-Term Memory (LSTM) network to process sequences of word indices and determine the sentiment of a review. It includes modules for data processing, model training, and evaluation.
## Data
Reviews from [IMDB dataset](https://ai.stanford.edu/~amaas/data/sentiment/)
## Features
- **Sentiment classification**: Classifies reviews into positive or negative sentiment
- **Data handling**: Processes and loads movie review data
- **Vocabulary building**: Constructs and saves a vocabulary for mapping words to indices
- **Model training**: Trains an LSTM model for sentiment analysis
- **Evaluation**: Evaluates the model and plots training and validation losses and accuracies
## File structure
- `config.py`: Contains configuration settings for the model.
- `classifier.py`: Defines the `SentimentClassifier` model.
- `dataset.py`: Contains the `MyDataset` class for handling data.
- `utils.py`: Utility functions for data processing, vocabulary handling, and accuracy calculation.
- `train.py`: Script to train the model.
- `predict.py`: Script to make predictions on new reviews.
- `saved/`: Directory for saving models and vocabulary.
## Installation
1. Clone the repository:
```bash
git clone https://github.com/yourusername/sentiment-classifier.git
```
2. Navigate into the project directory:
```bash
cd sentiment-classifier
```
3. Install the required dependencies:
```bash
pip install torch torchvision torchtext matplotlib
```
## Usage
### Training the Model
To train the model, run the following command:
```bash
python train.py
```
This will:
1. Load training and testing data.
2. Build or load the vocabulary.
3. Train the model and save the best-performing model.
4. Plot and save training and validation losses and accuracies.
### Making Predictions
To classify a review, use the `predict.py` script. Provide the review text as a command-line argument:
```bash
python predict.py "This movie was absolutely fantastic!"
```
This will output the classification of the review as either `Positive` or `Negative`.
## Configuration
Configuration settings are located in `config.py`. You can adjust parameters such as vocabulary size, embedding dimension, hidden size, and learning rate.
## Requirements
- Python 3.7+
- PyTorch
- TorchText
- Matplotlib