Sentiment-Analysis-BERT is an end-to-end tool designed for analyzing the sentiment of tweets using BERT technology. This application makes it simple to perform sentiment analysis with detailed results. You can understand how people feel about various topics based on their tweets. The tool includes preprocessing steps, model training, and evaluation, providing you with visual results like classification reports and word clouds.

To get started with Sentiment-Analysis-BERT, follow these simple steps:

1. Visit the Downloads Page: Click the link below to access all available versions: Download from Releases

2. Download: Choose the latest version from the page. It will typically be labeled with the highest version number.

3. Install the Software: After downloading, open the file and follow the on-screen instructions to install.

4. Run the Application: Once installed, open the application.

5. Input Your Tweets: Type or paste the tweets you want to analyze within the provided text box.

6. Analyze Results: Click the “Analyze” button to see the results. You will receive insights on sentiment along with visual reports.

• Operating System: Windows 10 or later, macOS 10.15 or later
• Memory: At least 4 GB of RAM
• Storage: 500 MB of available disk space
• Python: Version 3.7 or later (if using from source)
• Internet Connection: Required for downloading model files and analyzing data

• Sentiment Analysis: Understand sentiments (positive, negative, neutral) from tweets.
• Data Visualization: View results through classification reports and confusion matrices.
• ROC Curves: Evaluate the model performance with clear visual feedback.
• Word Clouds: Visual representation of common words in sentiments.
• User-Friendly Interface: Designed for everyone, regardless of technical knowledge.

The tool generates interesting visual reports, making it easy to grasp how each tweet influences sentiment. You’ll see confusion matrices that show correct and incorrect predictions. ROC curves provide insights into the effectiveness of the model.

• Use Relevant Tweets: For best results, choose recent and relevant tweets. Avoid tweets with mixed topics.
• Keep it Simple: Start with short tweets to understand how the tool works. As you grow comfortable, dive into longer tweets for deeper analysis.
• Explore the Outputs: Spend time looking at the generated graphs and reports. They can provide you with trends and important insights.

If you wish to contribute to the project, you can clone the repository, make your changes, and submit a pull request. Contributions are welcome, including bug fixes, new features, and documentation improvements.

If you encounter issues or have questions while using Sentiment-Analysis-BERT, you can reach out for support. Check the "Issues" section of the repository for solutions to common problems. You can also leave your questions directly there.

• Documentation: Access a full guide and instructions in the repository.
• Tutorials: Watch tutorials available online that explain sentiment analysis and how to use the tool effectively.

We would like to thank the contributors who made this project possible, as well as the creators of the BERT model and the Hugging Face library for their amazing work in natural language processing.

Stay tuned for future releases. We aim to enhance the application with new features, including advanced sentiment analytics and multi-language support. Check the releases page regularly for updates.

You can download the latest version of Sentiment-Analysis-BERT from the following link: Download from Releases