A Transformer-based library for SocialNLP tasks.
Currently supports:
| Task | Languages |
|---|---|
| Sentiment Analysis | es, en, it, pt |
| Hate Speech Detection | es, en, it, pt |
| Irony Detection | es, en, it, pt |
| Emotion Analysis | es, en, it, pt |
| NER & POS tagging | es, en |
| Contextualized Hate Speech Detection | es |
| Targeted Sentiment Analysis | es |
Just do pip install pysentimiento and start using it:
from pysentimiento import create_analyzer
analyzer = create_analyzer(task="sentiment", lang="es")
analyzer.predict("Qué gran jugador es Messi")
# returns AnalyzerOutput(output=POS, probas={POS: 0.998, NEG: 0.002, NEU: 0.000})
analyzer.predict("Esto es pésimo")
# returns AnalyzerOutput(output=NEG, probas={NEG: 0.999, POS: 0.001, NEU: 0.000})
analyzer.predict("Qué es esto?")
# returns AnalyzerOutput(output=NEU, probas={NEU: 0.993, NEG: 0.005, POS: 0.002})
analyzer.predict("jejeje no te creo mucho")
# AnalyzerOutput(output=NEG, probas={NEG: 0.587, NEU: 0.408, POS: 0.005})
"""
Emotion Analysis in English
"""
emotion_analyzer = create_analyzer(task="emotion", lang="en")
emotion_analyzer.predict("yayyy")
# returns AnalyzerOutput(output=joy, probas={joy: 0.723, others: 0.198, surprise: 0.038, disgust: 0.011, sadness: 0.011, fear: 0.010, anger: 0.009})
emotion_analyzer.predict("fuck off")
# returns AnalyzerOutput(output=anger, probas={anger: 0.798, surprise: 0.055, fear: 0.040, disgust: 0.036, joy: 0.028, others: 0.023, sadness: 0.019})
"""
Hate Speech (misogyny & racism)
"""
hate_speech_analyzer = create_analyzer(task="hate_speech", lang="es")
hate_speech_analyzer.predict("Esto es una mierda pero no es odio")
# returns AnalyzerOutput(output=[], probas={hateful: 0.022, targeted: 0.009, aggressive: 0.018})
hate_speech_analyzer.predict("Esto es odio porque los inmigrantes deben ser aniquilados")
# returns AnalyzerOutput(output=['hateful'], probas={hateful: 0.835, targeted: 0.008, aggressive: 0.476})
hate_speech_analyzer.predict("Vaya guarra barata y de poca monta es XXXX!")
# returns AnalyzerOutput(output=['hateful', 'targeted', 'aggressive'], probas={hateful: 0.987, targeted: 0.978, aggressive: 0.969})See TASKS for more details on the supported tasks and languages, and also for reported performance for each benchmarked model.
Also, check these notebooks with examples of how to use pysentimiento for each language:
pysentimiento features a tweet preprocessor specially suited for tweet classification with transformer-based models.
from pysentimiento.preprocessing import preprocess_tweet
# Replaces user handles and URLs by special tokens
preprocess_tweet("@perezjotaeme debería cambiar esto http://bit.ly/sarasa") # "@usuario debería cambiar esto url"
# Shortens repeated characters
preprocess_tweet("no entiendo naaaaaaaadaaaaaaaa", shorten=2) # "no entiendo naadaa"
# Normalizes laughters
preprocess_tweet("jajajajaajjajaajajaja no lo puedo creer ajajaj") # "jaja no lo puedo creer jaja"
# Handles hashtags
preprocess_tweet("esto es #UnaGenialidad")
# "esto es una genialidad"
# Handles emojis
preprocess_tweet("🎉🎉", lang="en")
# 'emoji party popper emoji emoji party popper emoji'- Clone and install
git clone https://github.com/pysentimiento/pysentimiento
pip install poetry
poetry shell
poetry install
- Run script to train models
Check TRAIN.md for further information on how to train your models
Note: you need access to the datasets, which are not public for the time being. Send us an email to get access to them.
- Upload models to Huggingface's Model Hub
Check "Model sharing and upload" instructions in huggingface docs.
pysentimiento is an open-source library. However, please be aware that models are trained with third-party datasets and are subject to their respective licenses, many of which are for non-commercial use
-
TASS Dataset license (License for Sentiment Analysis in Spanish, Emotion Analysis in Spanish & English)
-
SEMEval 2017 Dataset license (Sentiment Analysis in English)
-
LinCE Datasets (License for NER & POS tagging)
Please use the repository issue tracker to point out bugs and make suggestions (new models, use another datasets, some other languages, etc)
If you use pysentimiento in your work, please cite this paper
@misc{perez2021pysentimiento,
title={pysentimiento: A Python Toolkit for Opinion Mining and Social NLP tasks},
author={Juan Manuel Pérez and Mariela Rajngewerc and Juan Carlos Giudici and Damián A. Furman and Franco Luque and Laura Alonso Alemany and María Vanina Martínez},
year={2023},
eprint={2106.09462},a
archivePrefix={arXiv},
primaryClass={cs.CL}
}Also, pleace cite related pre-trained models and datasets for the specific models you use. Check REFERENCES for details.
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