https://github.com/narendraysf/lstmemotion-sentiment-analytics
An LSTM-based sentiment analysis model for classifying text emotions. Built with deep learning techniques to accurately detect and predict sentiment in text data.
https://github.com/narendraysf/lstmemotion-sentiment-analytics
ai emotion-detection-emotion-classification jupyter-notebook keras lstm-sentiment-analysis recurrent-neural-networks social-media-analysis tensorflow
Last synced: 3 months ago
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An LSTM-based sentiment analysis model for classifying text emotions. Built with deep learning techniques to accurately detect and predict sentiment in text data.
- Host: GitHub
- URL: https://github.com/narendraysf/lstmemotion-sentiment-analytics
- Owner: NarendraYSF
- License: gpl-3.0
- Created: 2025-01-27T06:02:28.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-02-17T18:23:43.000Z (4 months ago)
- Last Synced: 2025-03-26T03:30:34.496Z (3 months ago)
- Topics: ai, emotion-detection-emotion-classification, jupyter-notebook, keras, lstm-sentiment-analysis, recurrent-neural-networks, social-media-analysis, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 10 MB
- Stars: 20
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Sentiment Analysis on Movie Reviews
## Project Overview
This project implements a sentiment analysis model using Long Short-Term Memory (LSTM) networks to classify IMDB movie reviews as positive or negative. The model leverages deep learning techniques for text analysis, providing a robust solution to evaluate user sentiments.
## Features
- Binary classification of movie reviews (positive or negative).
- Preprocessing pipeline with tokenization and padding for textual data.
- LSTM-based architecture for sequential data learning.
- A user-friendly function for real-time sentiment predictions.
---
## Technologies Used
- **Programming Language:** Python
- **Libraries:**
- TensorFlow/Keras
- Pandas
- Scikit-learn
- **Tools:** Kaggle API for dataset retrieval
---
## Dataset
- **Source:** IMDB Dataset of 50K Movie Reviews
- **Access:** Downloaded via Kaggle API.
- **Structure:** Includes 50,000 movie reviews labeled as "positive" or "negative."
---
## Workflow
### 1. Dataset Handling
- **Download:** Use the Kaggle API to fetch the dataset.
- **Extraction:** Extract the CSV file from the zip archive.
### 2. Data Preprocessing
- Load the dataset using Pandas.
- Convert sentiment labels into numerical values (`positive: 1`, `negative: 0`).
### 3. Train-Test Split
- Split the data into 80% training and 20% testing subsets.
### 4. Tokenization and Padding
- Tokenize text to convert words into sequences of integers.
- Apply padding to ensure consistent sequence lengths for the LSTM model.
### 5. Model Architecture
The LSTM model consists of:
- **Embedding Layer:** Converts word indices to dense vectors.
- **LSTM Layer:** Processes sequential data for sentiment classification.
- **Dense Output Layer:** Sigmoid activation for binary classification.
### 6. Model Training
- Trained for 5 epochs with a batch size of 64.
- Validation split: 20% of the training data.
### 7. Model Evaluation
- Evaluate the trained model on the test dataset.
- Metrics: Accuracy and binary cross-entropy loss.
### 8. Sentiment Prediction
- Implement a function to classify the sentiment of user-provided reviews.
---
## Results
- The LSTM model achieved significant accuracy on the test data.
- Example Predictions:
- **Input:** "This movie was not so interesting." -> **Prediction:** Negative
- **Input:** "This movie was very amazing." -> **Prediction:** Positive
---
## How to Run
1. Clone this repository:
```bash
git clone https://github.com/NarendraYSF/LSTMEmotion-Sentiment-Analytics.git
```
2. Install the required dependencies:
```bash
pip install -r requirements.txt
```
3. Run the Jupyter Notebook to train, evaluate, and test the model.
---
## Future Scope
- **Expand Dataset:** Include more diverse reviews to improve generalization.
- **Optimize Hyperparameters:** Experiment with different learning rates, batch sizes, and epoch counts.
- **Alternative Architectures:** Explore GRU, Transformer-based models (e.g., BERT).
- **Deploy:** Build a web or API interface for real-time predictions.
---
## License
This project is licensed under the [GNU General Public License](https://opensource.org/licenses/GPL-3.0).