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bureaucratic-labs / dostoevsky

Sentiment analysis library for russian language

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Sentiment analysis library for russian language


Please note that

supports only Python 3.6+ on both Linux and Windows
$ pip install dostoevsky

Social network model [FastText]

This model was trained on RuSentiment dataset and achieves up to ~0.71 F1 score.


First of all, you'll need to download binary model:

$ python -m dostoevsky download fasttext-social-network-model

Then you can use sentiment analyzer:

from dostoevsky.tokenization import RegexTokenizer
from dostoevsky.models import FastTextSocialNetworkModel

tokenizer = RegexTokenizer() tokens = tokenizer.split('всё очень плохо') # [('всё', None), ('очень', None), ('плохо', None)]

model = FastTextSocialNetworkModel(tokenizer=tokenizer)

messages = [ 'привет', 'я люблю тебя!!', 'малолетние дебилы' ]

results = model.predict(messages, k=2)

for message, sentiment in zip(messages, results): # привет -> {'speech': 1.0000100135803223, 'skip': 0.0020607432816177607} # люблю тебя!! -> {'positive': 0.9886782765388489, 'skip': 0.005394937004894018} # малолетние дебилы -> {'negative': 0.9525841474533081, 'neutral': 0.13661839067935944}] print(message, '->', sentiment)

If you use the library in a research project, please include the following citation for the RuSentiment data: ``` @inproceedings{rogers-etal-2018-rusentiment, title = "{R}u{S}entiment: An Enriched Sentiment Analysis Dataset for Social Media in {R}ussian", author = "Rogers, Anna and Romanov, Alexey and Rumshisky, Anna and Volkova, Svitlana and Gronas, Mikhail and Gribov, Alex", booktitle = "Proceedings of the 27th International Conference on Computational Linguistics", month = aug, year = "2018", address = "Santa Fe, New Mexico, USA", publisher = "Association for Computational Linguistics", url = "", pages = "755--763", }

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