Comparison of neural network architectures for sentiment analysis of Russian tweets

Arkhipenko, K.; Kozlov, I.; Trofimovich, J.; Skorniakov, K.; Gomzin, A.; Turdakov, D.

Авторы: Arkhipenko K., Kozlov I., Trofimovich J., Skorniakov K., Gomzin A., Turdakov D.
Сборник: Computational Linguistics and Intellectual Technologies. International Conference "Dialog 2016" Proceedings
Редактор: Кобозева Ирина Михайловна
Том: 15
Год издания: 2016
Место издания: Изд-во РГГУ, Москва
Аннотация: The paper presents evaluation of three neural network based approaches to Twitter sentiment analysis task performed at SentiRuEval-2016. The task focuses on sentiment classification of tweets about banks and telecommunication companies. Our team submitted three solutions which are based on different supervised classifiers: Gated Recurrent Unit neural network (GRU), convolutional neural network (CNN), and SVM classifier with domain adaptation combined with previous two classifiers. We used vector representations of words obtained with word2vec model as features for classifiers. These classifiers were trained on labeled data provided by organizers of the evaluation. Additionally, we collected several million posts and comments from social networks for training word2vec model. According to evaluation results, GRU-based solution shows the best macro-averaged F1-score for both domains (banks and telecommunication companies) and also has the best micro-averaged F1-score for banks domain among all solutions submitted to SentiRuEval.
Добавил в систему: Гомзин Андрей Геннадьевич

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Comparison of neural network architectures for sentiment analysis of Russian tweetsстатья

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