README.md

---
language: it
license: mit
tags:
- sentiment
- Italian
---

# FEEL-IT: Emotion and Sentiment Classification for the Italian Language
## Abstract

Sentiment analysis is a common task to understand people's reactions online. Still, we often need more nuanced information: is the post negative because the user is angry or because they are sad?
An abundance of approaches has been introduced for tackling both tasks. However, at least for Italian, they all treat only one of the tasks at a time. We introduce FEEL-IT, a novel benchmark corpus of Italian Twitter posts annotated with four basic emotions: anger, fear, joy, sadness. By collapsing them, we can also do sentiment analysis. We evaluate our corpus on benchmark datasets for both emotion and sentiment classification, obtaining competitive results.
We release an open-source Python library, so researchers can use a model trained on FEEL-IT for inferring both sentiments and emotions from Italian text.

| Model | Download |
| ------ | ------ |
| `feel-it-italian-sentiment` | [Link](https://huggingface.co/MilaNLProc/feel-it-italian-sentiment) |
| `feel-it-italian-emotion` | Soon | 


## Usage

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("MilaNLProc/feel-it-italian-sentiment")
model = AutoModelForSequenceClassification.from_pretrained("MilaNLProc/feel-it-italian-sentiment")
```

## Citation
Please use the following bibtex entry if you use this model in your project:
```
@inproceedings{bianchi2021feel,
    title = {{"FEEL-IT: Emotion and Sentiment Classification for the Italian Language"}},
    author = "Bianchi, Federico  and
      Nozza, Debora  and
      Hovy, Dirk",
    booktitle = "Proceedings of the 11th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
    year = "2021",
    publisher = "Association for Computational Linguistics",
}
```