Dostoevsky

Sentiment analysis library for russian language

Install

Please note that

Dostoevsky

$ 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)

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", }