AmirhosseinHonardoust/Sentiment-Analysis-BERT
End-to-end sentiment analysis of tweets using BERT. Includes preprocessing, training, and evaluation with classification reports, confusion matrices, ROC curves, and word clouds. Demonstrates fine-tuning of transformer models for text classification with modular, reproducible code.
This project helps social media managers, brand analysts, or public relations professionals understand public sentiment. It takes raw tweet data and categorizes each tweet as 'positive', 'neutral', or 'negative'. The output includes performance reports, confusion matrices, and word clouds to visualize what words are associated with different sentiments.
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Use this if you need to quickly analyze the emotional tone of a large collection of tweets to gauge public opinion about a product, event, or brand.
Not ideal if you need to analyze sentiment from platforms other than Twitter or require real-time sentiment analysis for live feeds.
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Language
Python
License
MIT
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Last pushed
Sep 17, 2025
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