feel-it-italian-emotion
| Property | Value |
|---|---|
| Author | MilaNLProc |
| Base Architecture | UmBERTo |
| Dataset Size | 2037 annotated tweets |
| Performance | 73% accuracy on benchmark |
| Paper | FEEL-IT Paper |
What is feel-it-italian-emotion?
feel-it-italian-emotion is a specialized emotion classification model designed specifically for Italian text analysis. Built upon the UmBERTo architecture and trained on the FEEL-IT dataset, it can accurately identify four basic emotions: joy, fear, anger, and sadness in Italian text content. This model represents a significant advancement in Italian natural language processing, offering state-of-the-art performance for emotion detection tasks.
Implementation Details
The model is implemented using the Transformers library and has been fine-tuned on a carefully curated dataset of 2037 Italian tweets. It demonstrates robust performance with a Macro-F1 score of 0.57 and accuracy of 0.73 on the MultiEmotions-It benchmark, significantly outperforming the baseline Most Frequent Class (MFC) approach.
- Built on UmBERTo architecture
- Trained on diverse Twitter data
- Supports four emotion categories
- Easy integration with Hugging Face Transformers
Core Capabilities
- Emotion classification for Italian text
- Multi-label classification supporting joy, fear, anger, and sadness
- Cross-platform performance (tested on YouTube and Facebook content)
- Simple API integration with just a few lines of code
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically designed for Italian language emotion classification, filling a crucial gap in Italian NLP resources. It's unique in its ability to handle both sentiment and emotion classification tasks while maintaining high performance across different social media platforms.
Q: What are the recommended use cases?
The model is ideal for social media monitoring, customer feedback analysis, and general emotion detection in Italian text. It's particularly suited for applications requiring nuanced emotional understanding beyond simple sentiment analysis.
