This repository contains the source code for binary sentiment analysis of tweets in Russian using CNN. Word2Vec was used in order to set up pre-trained word embeddings for one-layer convolutional neural network, which was constructed using Keras with TensorFlow backend. Experiments showed F-measure score up to 78.14%.

The CNN architecture was inspired by approaches described in [3] and [4].

Citation:

@INPROCEEDINGS{Smetanin-SA-2019,
  author={Sergey Smetanin and Michail Komarov},
  booktitle={2019 IEEE 21st Conference on Business Informatics (CBI)},
  title={Sentiment Analysis of Product Reviews in Russian using Convolutional Neural Networks},
  year={2019},
  volume={01},
  number={},
  pages={482-486},
  doi={10.1109/CBI.2019.00062},
  ISSN={2378-1963},
  month={July}
}

The corpus of short texts in Russian based on Twitter messages is available at http://study.mokoron.com/ (and also described in [1]). The files positive.csv and negative.csv contain positively labelled and negatively labelled tweets, respectively.

To download trained CNN and Word2Vec models for Russian, please follow the link https://drive.google.com/open?id=13F4kn-DSmgDTc_6sjWubYKRCxvfoDSih.

1. Y. Rubtsova, "Constructing a Corpus for Sentiment Classification Training", Software & Systems, vol. 109, no. 1, pp. 72-78, 2015.
2. T. Mikolov, I. Sutskeve, K. Chen, G. Corrado and J. Dean, "Distributed Representations of Words and Phrases and their Compositionality", Advances in Neural Information Processing Systems, vol. 26, pp. 3111-3119, 2013.
3. Y. Zhang and B. Wallace, "A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification", arXiv preprint arXiv:1510.03820, 2015.
4. M. Cliche, "BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs", Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pp. 573-580, 2017.

• Keras documentation: https://keras.io/.
• Gensim documentatiob: https://radimrehurek.com/gensim/.
• Scikit-learn documentation: http://scikit-learn.org/stable/documentation.html.
• If you find any issues, please open a bug here on GitHub.

See LICENSE.