https://github.com/2003harsh/sentiment-analysis-using-bert
"Sentiment Analysis using BERT" utilizes advanced NLP with BERT for precise sentiment analysis, surpassing traditional methods like Word2Vec. Gain valuable insights into customer feedback and social sentiment effortlessly. 🚀💬 #NLP #SentimentAnalysis #BERT
https://github.com/2003harsh/sentiment-analysis-using-bert
beginner-friendly bert sentiment-analysis transformer
Last synced: 7 days ago
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"Sentiment Analysis using BERT" utilizes advanced NLP with BERT for precise sentiment analysis, surpassing traditional methods like Word2Vec. Gain valuable insights into customer feedback and social sentiment effortlessly. 🚀💬 #NLP #SentimentAnalysis #BERT
- Host: GitHub
- URL: https://github.com/2003harsh/sentiment-analysis-using-bert
- Owner: 2003HARSH
- License: mit
- Created: 2024-05-14T08:01:29.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-06-09T13:55:37.000Z (about 1 month ago)
- Last Synced: 2025-06-09T14:44:35.959Z (about 1 month ago)
- Topics: beginner-friendly, bert, sentiment-analysis, transformer
- Language: Jupyter Notebook
- Homepage:
- Size: 187 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Sentiment Analysis using BERT
## Overview
Sentiment Analysis using BERT is a Natural Language Processing (NLP) project focused on identifying sentiment from textual data. This project highlights how **context-aware embeddings**—generated using BERT—can vastly improve sentiment classification accuracy, especially in handling negations and subtle expressions like *"I didn't like the movie"* or *"The movie was not good."*
### Sample:
## Features
* **Context-Aware Embeddings**: BERT is used to generate contextualized word embeddings that understand the position and meaning of words in full sentences.
* **Custom Classification Head**: The `[CLS]` token output from BERT is passed into a custom **feedforward neural network (768 × 3)** for classifying sentiment into three categories (e.g., positive, negative, neutral).
* **Robust Against Negations**: Unlike traditional models like Bag of Words (BoW), TF-IDF, or Word2Vec, BERT embeddings preserve sentence structure and context. This allows the model to correctly classify difficult inputs like:
* `"I didn't like the movie"` → Negative
* `"The movie was not good"` → Negative
* **Bidirectional Understanding**: BERT’s bidirectional transformer architecture captures dependencies from both left and right contexts, providing deeper language understanding.
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## Getting Started
1. **Clone the repository**:
```bash
git clone https://github.com/2003HARSH/Sentiment-Analysis-using-BERT.git
cd Sentiment-Analysis-using-BERT
```
2. **Install the required dependencies**:
```bash
pip install -r requirements.txt
```
3. **Refer to the notebook**:
Open `sentiment-analysis-using-bert.ipynb` to see data preprocessing, model training, and evaluation.
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## Contributing
Contributions are welcome! If you'd like to contribute, please fork the repository and submit a pull request with your enhancements.
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## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
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Feel free to explore, experiment, and contribute to Sentiment Analysis using BERT. Let's unlock the power of language together! 🚀💬
**#SentimentAnalysis #BERT #NLP #MachineLearning #DeepLearning**
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Let me know if you want a version of this written in more technical paper style (for a portfolio or blog).