https://github.com/mohammed-majid/lstm_sentiment_amazon
Sentiment Analysis on Amazon Product Reviews (NLP)
https://github.com/mohammed-majid/lstm_sentiment_amazon
deep-learning fullstack lstm-neural-networks machine-learning nlp streamlit
Last synced: about 2 months ago
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Sentiment Analysis on Amazon Product Reviews (NLP)
- Host: GitHub
- URL: https://github.com/mohammed-majid/lstm_sentiment_amazon
- Owner: Mohammed-Majid
- Created: 2024-07-23T06:40:29.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-08-31T04:50:28.000Z (almost 2 years ago)
- Last Synced: 2025-03-06T19:45:44.035Z (over 1 year ago)
- Topics: deep-learning, fullstack, lstm-neural-networks, machine-learning, nlp, streamlit
- Language: Jupyter Notebook
- Homepage:
- Size: 8.39 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Amazon Review Sentiment Analysis (LSTM)
## Table of Contents
- [Overview](#overview)
- [Frontend](#Frontend)
- [Features](#features)
- [Installation](#installation)
- [Usage](#usage)
- [Acknowledgements](#acknowledgements)
## Overview
- This project is a sentiment analysis application that was built using amazon product review datasets.
- It uses a Long Short-Term Memory (LSTM) neural network to predict the sentiment (positive or negative) of a given review.
- The application was built using TensorFlow and Streamlit, effectively making it a full stack deep learning project.
## Frontend
## Features
- **Sentiment Prediction**: Classify the sentiment of a given review as positive or negative.
- **Translation**: Automatically detect the language of the review and translate it to English if necessary.
- **Confidence Score**: Display the model's prediction confidence score.
- **Prediction History**: View the history of predictions with the ability to expand and collapse the history section.
## Installation
To run this application locally, follow these steps:
1. **Clone the repository**:
```
git clone https://github.com/mohammed-majid/LSTM_Sentiment_amazon.git
```
2. **Install the required packages**:
```bash
pip install -r requirements.txt
```
4. **Download the pre-trained model and tokenizer** and place them in the project directory:
- `sentiment_analysis_model.h5`
- `tokenizer.pkl`
5. **Run the Streamlit application**:
```
streamlit run app.py
```
**or**
```
python3 -m streamlit run app.py
```
## Usage
1. **Open the Streamlit application** in your web browser.
2. **Enter a review text** in the provided text area.
3. **Click the "Predict Sentiment" button** to get the sentiment prediction and confidence score.
4. If the review is in a language other than English, the translated review will also be displayed.
5. **View the prediction history** by expanding the "View Prediction History" section.
## Acknowledgements
This project was developed using the following libraries and tools:
- [Pandas](https://pandas.pydata.org/)
- [NumPy](https://numpy.org/)
- [Keras](https://keras.io/)
- [TensorFlow](https://www.tensorflow.org/)
- [Streamlit](https://streamlit.io/)
- [Scikit-learn](https://scikit-learn.org/)
- [Langdetect](https://pypi.org/project/langdetect/)
- [Googletrans](https://pypi.org/project/googletrans/)
- [Pickle](https://docs.python.org/3/library/pickle.html)
### Side Note
- Considering the size of the dataset used for this project, I was unable to commit it to this repository. In case you want to check it out, [Press here.](https://kaggle.com/datasets/arhamrumi/amazon-product-reviews)