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: about 2 months 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 (8 months ago)
• Default Branch: main
• Last Pushed: 2024-06-16T04:59:00.000Z (7 months ago)
• Last Synced: 2024-06-16T05:47:29.141Z (7 months ago)
• Topics: beginner-friendly, bert, sentiment-analysis, transformer
• Language: Jupyter Notebook
• Homepage:
• Size: 185 KB
• Stars: 0
• 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 project aimed at analyzing the sentiment expressed in textual data. Leveraging the power of BERT (Bidirectional Encoder Representations from Transformers), this project provides a robust framework for understanding the sentiment of various forms of text, such as customer reviews, social media posts, and more.

### Sample:

![](https://github.com/2003HARSH/Sentiment-Analysis-using-BERT/blob/main/docs/static/bert_demo.jpeg)

## Features

- **Contextual Understanding**: BERT captures the contextual nuances of language, enabling more accurate sentiment analysis by considering the entire sentence rather than individual words.

  

- **Pre-trained Language Model**: BERT is pre-trained on vast amounts of text data, making it adept at understanding sentiment in diverse contexts.

  

- **Bidirectional Attention**: BERT's bidirectional architecture captures dependencies in both left and right contexts of each word, enhancing its ability to comprehend sentiment expressed in text.

  

- **Fine-Tuning Capabilities**: BERT can be fine-tuned on domain-specific data, tailoring its sentiment analysis capabilities to suit specific applications and achieve higher accuracy.

## Getting Started

1. Clone the repository:

   ```

   https://github.com/2003HARSH/Sentiment-Analysis-using-BERT.git

   cd Sentiment-Analysis-using-BERT

   ```

2. Install the required dependencies:

   ```

   pip install -r requirements.txt

   ```

3. Refer the sentiment-analysis-using-bert.ipynb file for detailed uasge tips:

## Contributing

Contributions are welcome! If you'd like to contribute to Sentiment Analysis using BERT, please fork the repository and submit a pull request with your changes.

## 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 #DataScience