Dostoevsky

Sentiment analysis library for russian language

Install

Please note that Dostoevsky supports only Python 3.7+ 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.

Usage

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)

Articles

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• 🇷🇺 Семантический анализ мнений о поправках к Конституции на основе данных ВКонтакте
• 🇷🇺 Как писать посты в стиле Артемия Лебедева? Подробный анализ телеграм-канала и кое-что еще

Feel free to extend this list with your article! ✨

Related projects

• David Dale BERT-based models for emotion detection and classification of toxicity

Citation

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 = "https://www.aclweb.org/anthology/C18-1064",
    pages = "755--763",
}