https://github.com/arpanpramanik2003/sentiment-analysis-lstm

This project performs sentiment analysis on IMDB movie reviews using an LSTM deep learning model. It processes text data, trains an LSTM network to classify reviews as positive or negative, and provides a prediction function for user input. The project utilizes TensorFlow, Keras, and Pandas for model building and data handling.
https://github.com/arpanpramanik2003/sentiment-analysis-lstm

classification deep-learning imdb-dataset imdb-reviews keras lstm-neural-networks machine-learning model-evaluation python sentiment-analysis tensorflow2

Last synced: 21 days ago
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This project performs sentiment analysis on IMDB movie reviews using an LSTM deep learning model. It processes text data, trains an LSTM network to classify reviews as positive or negative, and provides a prediction function for user input. The project utilizes TensorFlow, Keras, and Pandas for model building and data handling.

• Host: GitHub
• URL: https://github.com/arpanpramanik2003/sentiment-analysis-lstm
• Owner: arpanpramanik2003
• License: gpl-3.0
• Created: 2025-01-22T17:32:37.000Z (23 days ago)
• Default Branch: master
• Last Pushed: 2025-01-22T17:42:44.000Z (23 days ago)
• Last Synced: 2025-01-25T03:56:50.158Z (21 days ago)
• Topics: classification, deep-learning, imdb-dataset, imdb-reviews, keras, lstm-neural-networks, machine-learning, model-evaluation, python, sentiment-analysis, tensorflow2
• Language: Jupyter Notebook
• Homepage:
• Size: 37.1 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Sentiment Analysis on IMDB Movie Reviews

## Project Overview

This project focuses on performing sentiment analysis on IMDB movie reviews using deep learning techniques with an LSTM (Long Short-Term Memory) model. The dataset used contains 50,000 movie reviews labeled as positive or negative.

## Technologies Used

- Python

- TensorFlow/Keras

- Pandas

- Scikit-learn

- Kaggle API

## Dataset

The dataset used in this project is the **IMDB Dataset of 50K Movie Reviews**, which was downloaded from Kaggle.

## Steps Involved

1. **Dataset Downloading:**

   - The dataset is downloaded using the Kaggle API.

   - The zip file is extracted to access the CSV file.

2. **Data Preprocessing:**

   - The dataset is loaded using Pandas.

   - Sentiment labels are converted to numerical values (positive: 1, negative: 0).

3. **Train-Test Splitting:**

   - The dataset is split into training (80%) and testing (20%) sets.

4. **Tokenization and Padding:**

   - Tokenization is applied to convert text to sequences.

   - Padding is used to ensure uniform sequence length.

5. **LSTM Model Building:**

   - An LSTM model is created with the following layers:

     - Embedding layer

     - LSTM layer

     - Dense output layer with a sigmoid activation function

   - Model is compiled using binary cross-entropy loss and the Adam optimizer.

6. **Model Training:**

   - The model is trained with 5 epochs and a batch size of 64.

   - Validation split of 20% is used.

7. **Model Evaluation:**

   - The model is evaluated on the test data.

   - Accuracy and loss metrics are reported.

8. **Prediction Function:**

   - A function is implemented to predict sentiment based on user input reviews.

## Results

- The model achieved satisfactory accuracy on the test set.

- Example predictions:

  - "This movie was not so interesting." -> Negative

  - "This movie was very amazing." -> Positive

## How to Run the Project

1. Clone the repository from GitHub.

2. Install required dependencies using:

   ```bash

   pip install -r requirements.txt

   ```

3. Run the Jupyter Notebook to train and evaluate the model.

## Future Improvements

- Increase the dataset size to enhance model performance.

- Tune hyperparameters for better accuracy.

- Experiment with different neural network architectures.

## License

This project is under the MIT License.

## Author

**Arpan Pramanik**