roberta-english-book-reviews-sentiment
| Property | Value |
|---|---|
| Parameter Count | 355M parameters |
| Model Type | Text Classification |
| License | MIT |
| Paper | Research Paper |
What is roberta-english-book-reviews-sentiment?
This is a specialized sentiment analysis model built on RoBERTa architecture, fine-tuned specifically for analyzing book reviews and story content. The model performs three-class sentiment classification (positive, negative, neutral) and has shown impressive performance metrics, particularly for review analysis with 94% accuracy.
Implementation Details
The model was fine-tuned using two distinct datasets: annotated sentences from book reviews in English and annotated paragraphs from amateur writers' stories. It leverages the RoBERTa architecture with 355M parameters and uses PyTorch with Safetensors for efficient processing.
- Achieves 79% accuracy on book-related content with 1,666 examples
- Demonstrates 94% accuracy on reviews with 205 examples
- Implements three-class classification system
Core Capabilities
- High precision in negative sentiment detection (0.83-0.89)
- Strong positive sentiment recognition (0.79-0.94 precision)
- Balanced neutral class handling (0.68-0.96 precision)
- Optimized for both book reviews and story content analysis
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized focus on literary content, particularly book reviews and stories, with separate performance metrics for both use cases. Its three-class classification system provides more nuanced sentiment analysis compared to binary classifiers.
Q: What are the recommended use cases?
The model is best suited for analyzing book reviews, literary criticism, and amateur writing content. It's particularly effective for applications requiring sentiment analysis in publishing, literary analysis, and reader feedback processing.
