{"id":20619365,"url":"https://github.com/mohammed-majid/lstm_sentiment_amazon","last_synced_at":"2026-05-06T00:06:39.197Z","repository":{"id":249770629,"uuid":"832499963","full_name":"Mohammed-Majid/LSTM_sentiment_amazon","owner":"Mohammed-Majid","description":"Sentiment Analysis on Amazon Product Reviews (NLP)","archived":false,"fork":false,"pushed_at":"2024-08-31T04:50:28.000Z","size":8798,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-06T19:45:44.035Z","etag":null,"topics":["deep-learning","fullstack","lstm-neural-networks","machine-learning","nlp","streamlit"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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[Features](#features)\n- [Installation](#installation)\n- [Usage](#usage)\n- [Acknowledgements](#acknowledgements)\n\n## Overview\n\n- This project is a sentiment analysis application that was built using amazon product review datasets. \n- It uses a Long Short-Term Memory (LSTM) neural network to predict the sentiment (positive or negative) of a given review.\n- The application was built using TensorFlow and Streamlit, effectively making it a full stack deep learning project.\n\n## Frontend\n\u003cimg width=\"839\" alt=\"Screen Shot 2024-07-23 at 10 45 27 AM\" src=\"https://github.com/user-attachments/assets/85d55ce4-0d39-4ce8-8ead-157c04e0d199\"\u003e\n\n## Features\n\n- **Sentiment Prediction**: Classify the sentiment of a given review as positive or negative.\n- **Translation**: Automatically detect the language of the review and translate it to English if necessary.\n- **Confidence Score**: Display the model's prediction confidence score.\n- **Prediction History**: View the history of predictions with the ability to expand and collapse the history section.\n\n## Installation\n\nTo run this application locally, follow these steps:\n\n1. **Clone the repository**:\n    ```\n    git clone https://github.com/mohammed-majid/LSTM_Sentiment_amazon.git\n    ```\n\n2. **Install the required packages**:\n    ```bash\n    pip install -r requirements.txt\n    ```\n\n4. **Download the pre-trained model and tokenizer** and place them in the project directory:\n    - `sentiment_analysis_model.h5`\n    - `tokenizer.pkl`\n\n5. **Run the Streamlit application**:\n    ```\n    streamlit run app.py\n    ```\n    **or**\n    ```\n    python3 -m streamlit run app.py\n    ```\n\n## Usage\n\n1. **Open the Streamlit application** in your web browser.\n\n2. **Enter a review text** in the provided text area.\n\n3. **Click the \"Predict Sentiment\" button** to get the sentiment prediction and confidence score.\n\n4. If the review is in a language other than English, the translated review will also be displayed.\n\n5. **View the prediction history** by expanding the \"View Prediction History\" section.\n\n## Acknowledgements\n\nThis project was developed using the following libraries and tools:\n- [Pandas](https://pandas.pydata.org/)\n- [NumPy](https://numpy.org/)\n- [Keras](https://keras.io/)\n- [TensorFlow](https://www.tensorflow.org/)\n- [Streamlit](https://streamlit.io/)\n- [Scikit-learn](https://scikit-learn.org/)\n- [Langdetect](https://pypi.org/project/langdetect/)\n- [Googletrans](https://pypi.org/project/googletrans/)\n- [Pickle](https://docs.python.org/3/library/pickle.html)\n\n### Side Note\n- 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)\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmohammed-majid%2Flstm_sentiment_amazon","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmohammed-majid%2Flstm_sentiment_amazon","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmohammed-majid%2Flstm_sentiment_amazon/lists"}