maisМоделирование и анализ информационных системModeling and Analysis of Information Systems1818-10152313-5417Yaroslavl State University10.18255/1818-1015-2022-2-134-147mais-1651Research ArticleTheory of DataРекурсивный алгоритм определения тональности предложений на русском языкеRecursive Sentiment Detection Algorithm for Russian Sentenceshttps://orcid.org/0000-0003-0116-4739ПолетаевАнатолий ЮрьевичPoletaevAnatoliy Y.anatoliy-poletaev@mail.ruhttps://orcid.org/0000-0003-3984-8423ПарамоновИлья ВячеславовичParamonovIlya V.ilya.paramonov@fruct.orgЯрославский государственный университет им. П. Г. ДемидоваРоссияP. G. Demidov Yaroslavl State UniversityRussian Federation202217062022292134147Copyright © Полетаев А.Ю., Парамонов И.В., 20222022Полетаев А.Ю., Парамонов И.В.Poletaev A.Y., Paramonov I.V.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.mais-journal.ru/jour/article/view/1651

В статье рассматривается задача определения тональности русскоязычных предложений. Тональность понимается как отношение автора к теме предложения. В данном исследовании учитываются три варианта тональности - положительная, отрицательная и нейтральная, т. е. решается задача классификации с тремя классами. В статье предлагается алгоритм определения тональности предложения на русском языке, основанный на семантических правилах. В основе алгоритма лежит предположение о том, что тональность фразы может быть определена на основе тональностей её составляющих с помощью рекурсивного применения семантических правил к составным частям фразы, представленным в виде синтаксического дерева. Набор семантических правил, используемых алгоритмом, был составлен в результате обсуждений с экспертами-филологами. Эксперименты показали, что предложенный рекурсивный алгоритм даёт несколько худший результат на корпусе отзывов на отели по сравнению с подходом, основанным на правилах, ранее адаптированным авторами для русского языка: взвешенная $F_1$-мера составила 0.75 и 0.78 соответственно. Для оценки качества работы алгоритма на сложных предложениях был создан корпус OpenSentimentCorpus, основанный на OpenCorpora - открытом корпусе предложений из новостных статей и публицистики. На корпусе OpenSentimentCorpus рекурсивный алгоритм работает лучше, чем адаптированный подход: $F_1$-мера составила 0.70 и 0.63 соответственно. Таким образом, предложенный в данной работе алгоритм имеет преимущество в случае более сложных предложений с более тонкими способами выражения тональности.

The article is devoted to the task of sentiment detection of Russian sentences. The sentiment is conceived as the author's attitude to the topic of a sentence. This assay considers positive, neutral, and negative sentiment classes, i.e., the task of three-classes classification is solved. The article introduces a rule-based sentiment detection algorithm for Russian sentences. The algorithm is based on the assumption that the sentiment of a phrase can be determined by the sentiments of its parts by the recursive application of appropriate semantic rules to the sentiments of its parts organized as a constituency parse tree. The utilized set of semantic rules was constructed based on a discussion with experts in linguistics. The experiments showed that the proposed recursive algorithm performs slightly worse on the hotel reviews corpus than the adapted rule-based approach: weighted $F_1$-measures are 0.75 and 0.78, respectively. To measure the algorithm efficiency on complex sentences, we created OpenSentimentCorpus based on OpenCorpora, an open corpus of sentences extracted from Russian news and periodicals. On OpenSentimentCorpus the recursive algorithm performs better than the adapted approach does: $F_1$-measures are 0.70 and 0.63, respectively. This indicates that the proposed algorithm has an advantage in case of more complex sentences with more subtle ways of expressing the sentiment.

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The authors declare that there are no conflicts of interest present.