roberta-targeted-sentiment-classification-newsarticles
| Property | Value |
|---|---|
| Author | fhamborg |
| Paper | EACL 2021 |
| Implementation | NewsSentiment PyPI package |
| Dataset Size | 10k+ annotated news sentences |
What is roberta-targeted-sentiment-classification-newsarticles?
This is a specialized sentiment analysis model designed specifically for news articles, built on RoBERTa architecture. Unlike traditional sentiment classifiers, it performs target-dependent sentiment classification, meaning it can analyze sentiment specifically related to particular entities within a text. The model was trained on a carefully curated dataset of over 10,000 news article sentences, each validated by at least 5 annotators.
Implementation Details
The model is primarily accessible through the NewsSentiment PyPI package, as the Huggingface Hub platform doesn't directly support target-dependent sentiment classification inputs. It's built on robust methodology detailed in the EACL 2021 paper by Hamborg and Donnay.
- Target-specific sentiment analysis capability
- Trained on high-quality news article dataset
- Multiple annotator validation
- Available through dedicated PyPI package
Core Capabilities
- Target-dependent sentiment classification in news contexts
- Handles complex sentiment relationships (e.g., "I like Bert, but I hate Robert")
- Specialized for news article content
- High accuracy through multiple annotator validation
Frequently Asked Questions
Q: What makes this model unique?
The model's ability to perform target-dependent sentiment analysis sets it apart, allowing for more nuanced sentiment analysis in complex sentences where different entities may have different associated sentiments.
Q: What are the recommended use cases?
The model is specifically designed for analyzing sentiment in news articles, particularly when you need to understand sentiment towards specific entities or targets within the text. It's recommended to use the NewsSentiment PyPI package for implementation.
