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.
Code comes from HuggingFace and thus our License is an MIT license.
For models restrictions may apply on the data (which are derived from existing datasets) or Twitter (main data source). We refer users to the original licenses accompanying each dataset and Twitter regulations.
Fill out this form to access the dataset, an email notification will be sent with the data.
- Emotion Classification (fear, joy, sadness, anger) in Italian
- Sentiment Classification (positive, negative) in Italian
pip install -U feel-it| Name | Link |
|---|---|
| Sentiment and Emotion Classification (stable v1.0.2) |
|
The two classifiers are very easy to use. You can also directly use our colab tutorial!
from feel_it import EmotionClassifier, SentimentClassifier
emotion_classifier = EmotionClassifier()
emotion_classifier.predict(["sono molto felice", "ma che cazzo vuoi", "sono molto triste"])
>> ['joy', 'anger', 'sadness']
sentiment_classifier = SentimentClassifier()
sentiment_classifier.predict(["sono molto felice", "ma che cazzo vuoi", "sono molto triste"])
>> ['positive', 'negative', 'negative']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",
}
You can find our HF Models here:
| Name | Link |
|---|---|
| MilaNLProc/feel-it-italian-emotion | Emotion Model |
| MilaNLProc/feel-it-italian-sentiment | Sentiment Model |
- Federico Bianchi <[email protected]> Bocconi University
- Debora Nozza <[email protected]> Bocconi University
- Dirk Hovy <[email protected]> Bocconi University
Remember that this is a research tool :)
- https://github.com/RacheleSprugnoli/Esercitazioni_SA (very nice dataset for emotion analysis)
This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.
