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--- |
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language: it |
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license: mit |
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tags: |
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- sentiment |
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- Italian |
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--- |
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# FEEL-IT: Emotion and Sentiment Classification for the Italian Language |
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## Abstract |
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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? |
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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. |
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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. |
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| Model | Download | |
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| ------ | ------ | |
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| `feel-it-italian-sentiment` | [Link](https://huggingface.co/MilaNLProc/feel-it-italian-sentiment) | |
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| `feel-it-italian-emotion` | [Link](https://huggingface.co/MilaNLProc/feel-it-italian-emotion) | |
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## Model |
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The feel-it-italian-emotion model performs emotion classification (joy, fear, anger, sadness). We fine-tuned the [UmBERTo model](https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1) on our new dataset (i.e., FEEL-IT) obtaining state-of-the-art performances on different data sets. |
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## Data |
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Our data has been collected by annotating tweets from a broad range of topics. In total, we have 2037 tweets annotated with an emotion label. More details can be found in our paper. |
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## Performance |
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Soon |
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## Usage |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained("MilaNLProc/feel-it-italian-emotion") |
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model = AutoModelForSequenceClassification.from_pretrained("MilaNLProc/feel-it-italian-emotion") |
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``` |
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## Citation |
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Please use the following bibtex entry if you use this model in your project: |
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``` |
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@inproceedings{bianchi2021feel, |
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title = {{"FEEL-IT: Emotion and Sentiment Classification for the Italian Language"}}, |
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author = "Bianchi, Federico and Nozza, Debora and Hovy, Dirk", |
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booktitle = "Proceedings of the 11th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis", |
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year = "2021", |
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publisher = "Association for Computational Linguistics", |
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} |
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``` |