twitter-roberta-base-sentiment-latest
| Property | Value |
|---|---|
| Author | cardiffnlp |
| Paper | TimeLMs paper |
| Downloads | 4.9M+ |
| Framework | PyTorch & TensorFlow |
What is twitter-roberta-base-sentiment-latest?
This is an advanced sentiment analysis model built on RoBERTa-base architecture, specifically trained on approximately 124 million tweets collected between January 2018 and December 2021. The model is fine-tuned using the TweetEval benchmark and provides sophisticated sentiment classification into three categories: Negative, Neutral, and Positive.
Implementation Details
The model implements a transformer-based architecture with special preprocessing capabilities for Twitter-specific content, including handling of usernames and URLs. It supports both PyTorch and TensorFlow frameworks, making it versatile for different development environments.
- Pre-trained on a massive dataset of 124M tweets
- Implements automatic preprocessing of Twitter-specific content
- Provides probability scores for each sentiment category
- Supports both PyTorch and TensorFlow implementations
Core Capabilities
- Three-class sentiment classification (Negative, Neutral, Positive)
- Handles Twitter-specific text preprocessing automatically
- Returns confidence scores for each prediction
- Integrates seamlessly with the Hugging Face transformers library
- Supports batch processing for efficient analysis
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its specialized training on recent Twitter data, making it particularly effective for social media sentiment analysis. The temporal nature of the training data (2018-2021) ensures it understands contemporary language patterns and expressions.
Q: What are the recommended use cases?
The model is ideal for social media sentiment analysis, brand monitoring, public opinion tracking, and large-scale Twitter data analysis. It's particularly effective for applications requiring understanding of modern social media language and expressions.
