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https://github.com/2003harsh/sentiment-analysis-on-movie-reviews
🎬 Analyze movie reviews sentiment in real-time with "Sentiment Analysis on Movie Reviews using Word2Vec"! Powered by advanced NLP and deployed using Streamlit, this app categorizes reviews as positive or negative. Perfect for film enthusiasts and industry professionals! 🍿📊
https://github.com/2003harsh/sentiment-analysis-on-movie-reviews
natural-language-processing sentiment-analysis word2vec-embeddinngs
Last synced: about 2 months ago
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🎬 Analyze movie reviews sentiment in real-time with "Sentiment Analysis on Movie Reviews using Word2Vec"! Powered by advanced NLP and deployed using Streamlit, this app categorizes reviews as positive or negative. Perfect for film enthusiasts and industry professionals! 🍿📊
- Host: GitHub
- URL: https://github.com/2003harsh/sentiment-analysis-on-movie-reviews
- Owner: 2003HARSH
- License: mit
- Created: 2024-04-11T06:53:42.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2024-05-11T13:33:50.000Z (8 months ago)
- Last Synced: 2024-05-11T14:37:43.834Z (8 months ago)
- Topics: natural-language-processing, sentiment-analysis, word2vec-embeddinngs
- Language: Python
- Homepage: https://sentiment-analysis-on-movie-reviews-word2vec.streamlit.app/
- Size: 52.5 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Sentiment Analysis on Movie Reviews using Word2Vec
Sentiment Analysis on Movie Reviews is a natural language processing (NLP) project aimed at analyzing the sentiment of movie reviews using Word2Vec embedding techniques. This project utilizes machine learning and deep learning algorithms to classify movie reviews as positive or negative based on the sentiment conveyed in the text.
## Introduction
Sentiment analysis is a valuable application of natural language processing (NLP) that aims to determine the sentiment expressed in a piece of text. In this project, we focus specifically on movie reviews and use Word2Vec embeddings to represent words in a continuous vector space. This allows us to capture semantic relationships between words and improve the accuracy of sentiment classification.
## Features
- Preprocessing of movie review text data 🧹
- Word2Vec embedding of movie review text 📊
- Training and evaluation of sentiment classification models 📈
- Visualization of Word2Vec embeddings and model performance metrics 📊
## How to Use
1. **Clone Repository:**
```
git clone https://github.com/2003HARSH/Sentiment-Analysis-on-movie-reviews.git
cd Sentiment-Analysis-on-movie-reviews
```
2. **Install Dependencies:**
```
pip install -r requirements.txt
```
3. **Run the Streamlit App:**
```
streamlit run app.py
```
4. **Access the App:**
Open your browser and go to `http://localhost:8501`.
## Deployment
This project is deployed using Streamlit. You can access the deployed application [here](https://sentiment-analysis-on-movie-reviews-word2vec.streamlit.app/).
## Contributing
Contributions are welcome! If you'd like to contribute to this project, please follow these steps:
1. Fork this repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes and commit them.
4. Push your changes to your fork.
5. Submit a pull request with a detailed description of your changes.
## Technologies
- Python: The primary programming language used for development and scripting.
- Streamlit: Used for deploying the interactive web application for sentiment analysis.
- Word2Vec: Utilized for word embedding to represent words in a continuous vector space.
- Pandas: Used for data manipulation and analysis.
- NumPy: Utilized for numerical computing and array operations.
- scikit-learn: Employed for machine learning algorithms and model evaluation.
- Matplotlib: Utilized for data visualization and plotting.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.