Downloadtwitter-roberta-base-sentiment-latest is a RoBERTa-based transformer model fine-tuned on over 124 million tweets collected between 2018 and 2021. Designed for sentiment analysis in English, it categorizes tweets as Negative, Neutral, or Positive. The model is optimized using the TweetEval benchmark and integrated with the TweetNLP ecosystem for seamless deployment. Its training emphasizes real-world, social media content, making it highly effective for analyzing informal or noisy text. This updated version improves performance over earlier Twitter sentiment models. It supports both PyTorch and TensorFlow and includes example pipelines for quick implementation. With strong classification accuracy and ease of use, it’s ideal for social media monitoring, brand sentiment tracking, and public opinion research.
Features
- Trained on ~124 million English tweets from 2018–2021
- Fine-tuned for sentiment classification: Negative, Neutral, Positive
- Optimized using the TweetEval benchmark dataset
- Compatible with both PyTorch and TensorFlow
- Includes built-in preprocessing for usernames and URLs
- Offers easy integration with Hugging Face Transformers and TweetNLP
- Returns softmax-based sentiment confidence scores
- Updated in 2022 for improved social media text understanding
Project Samples
