maisМоделирование и анализ информационных системModeling and Analysis of Information Systems1818-10152313-5417Yaroslavl State University10.18255/1818-1015-2023-4-394-417UVOKLCmais-1828Research ArticleArtificial IntelligenceАлгоритм определения тональности предложений публицистического стиля на русском языке на основе семантических правилSemantic rule-based sentiment detection algorithm for Russian publicism sentenceshttps://orcid.org/0000-0003-0116-4739ПолетаевАнатолий ЮрьевичPoletaevAnatoliy Y.anatoliy-poletaev@mail.ruhttps://orcid.org/0000-0003-3984-8423ПарамоновИлья ВячеславовичParamonovIlya V.ilya.paramonov@fruct.orghttps://orcid.org/0000-0001-6600-2971БойчукЕлена ИгоревнаBoychukElena I.elena-boychouk@rambler.ruЯрославский государственный университет им. П.Г. ДемидоваРоссияP.G. Demidov Yaroslavl State UniversityRussian Federation202311122023304394417Copyright © Полетаев А.Ю., Парамонов И.В., Бойчук Е.И., 20232023Полетаев А.Ю., Парамонов И.В., Бойчук Е.И.Poletaev A.Y., Paramonov I.V., Boychuk E.I.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/1828

Статья посвящена задаче определения тональности предложения на русском языке, понимаемой как отношение автора предложения к его теме, выраженное с помощью языковых средств. В настоящий момент большинство исследований по этой теме проводятся на текстах разговорного стиля речи, что ограничивает применимость их результатов для других стилей, в частности, публицистического. Для того, чтобы заполнить этот пробел, авторами был разработан алгоритм определения тональности, ориентированный на применение к предложениям публицистического стиля речи. Алгоритм рекурсивно применяет подходящие правила к составным частям предложения, представленным в виде дерева синтаксических единиц. Большинство правил было построено на основе знаний эксперта-филолога относительно средств выражения тональности, известных русской лингвистике, и выбора тех из них, которые достаточно формализованы для того, чтобы их можно было алгоритмизировать с использованием генерируемых в рамках алгоритма деревьев синтаксических единиц. Также применялись дерево решений и тональный словарь. В статье приведены результаты эксперимента по апробации предложенного алгоритма на корпусе предложений публицистического стиля OpenSentimentCorpus, F-мера составила 0.80, а также результаты анализа ошибок алгоритма.

The article is devoted to the task of sentiment detecton of Russian sentences, which is understood as the author’s attitude on the sentence topic expressed through linguistic expression features. Today most studies on this subject utilize texts of colloquial style, limiting the applicability of their results to other styles of speech, particularly to the publicism. To fill the gap, the authors developed a novel publisism sentences oriented sentiment detection algorithm. The algorithm recursively applies appropriate rules to sentence parts represented as constituency trees. Most of the rules were proposed by a philology expert, based on knowledge on the expression features from Russian philology, and algorithmized using constituency trees generated by the algorithm. A decision tree and a sentiment vocabulary are also used in the work. The article contains the results of evaluation of the algorithm on the publicism sentences corpus OpenSentimentCorpus, F-measure is 0.80. The results of errors analysis are also presented.

анализ тональностиопределение тональностисемантические правилапублицистический стильдерево синтаксических единицsentiment analysissentiment detectionsemantic rulespublicismconstituency treeИсследование выполнено за счет гранта Российского научного фонда №23-21-00495.The reported study was funded by the grant of Russian Science Foundation No. 23-21-00495.ReferencesB. Liu, Sentiment Analysis and Opinion Mining. Springer, 2022.B. Liu, Sentiment Analysis and Opinion Mining. Springer, 2022.A. Dvoybikova, A. Karpov, and O. Verkholyak, “Analytical Review of Methods for Identifying Emotions in Text Data,” in 3rd International Conference on R. Piotrowski's Readings in Language Engineering and Applied Linguistics, PRLEAL 2019, 2020, pp. 8–21.A. Dvoybikova, A. Karpov, and O. Verkholyak, “Analytical Review of Methods for Identifying Emotions in Text Data,” in 3rd International Conference on R. Piotrowski's Readings in Language Engineering and Applied Linguistics, PRLEAL 2019, 2020, pp. 8–21.S. Smetanin and M. Komarov, “Deep Transfer Learning Baselines for Sentiment Analysis in Russian,” Information Processing & Management, vol. 58, no. 3, p. 102484, 2021.S. Smetanin and M. Komarov, “Deep Transfer Learning Baselines for Sentiment Analysis in Russian,” Information Processing & Management, vol. 58, no. 3, p. 102484, 2021.K. Nursakitov, A. Bekishev, S. Kumargazhanova, and A. Urkumbaeva, “Review of Methods for Determining the Tonation of Texts in Natural Languages,” Bulletin of Shakarim University. Technical Sciences, no. 1 (9), pp. 59–67, 2023.K. Nursakitov, A. Bekishev, S. Kumargazhanova, and A. Urkumbaeva, “Review of Methods for Determining the Tonation of Texts in Natural Languages,” Bulletin of Shakarim University. Technical Sciences, no. 1 (9), pp. 59–67, 2023.M. S. Bacsarslan and F. Kayaalp, “Sentiment Analysis on Social Media Reviews Datasets with Deep Learning Approach,” Sakarya University Journal of Computer and Information Sciences, vol. 4, no. 1, pp. 35–49, 2021.M. S. Bacsarslan and F. Kayaalp, “Sentiment Analysis on Social Media Reviews Datasets with Deep Learning Approach,” Sakarya University Journal of Computer and Information Sciences, vol. 4, no. 1, pp. 35–49, 2021.M. Wankhade, A. C. S. Rao, and C. Kulkarni, “A Survey on Sentiment Analysis Methods, Applications, and Challenges,” Artificial Intelligence Review, vol. 55, no. 7, pp. 5731–5780, 2022.M. Wankhade, A. C. S. Rao, and C. Kulkarni, “A Survey on Sentiment Analysis Methods, Applications, and Challenges,” Artificial Intelligence Review, vol. 55, no. 7, pp. 5731–5780, 2022.E. N. Tulupova and E. V. Golovina, “Lexico-Stylistic Percularities of Tourist's Internet Commentary,” Philology. Theory & Practice, vol. 12, no. 5, pp. 257–261, 2019.E. N. Tulupova and E. V. Golovina, “Lexico-Stylistic Percularities of Tourist's Internet Commentary,” Philology. Theory & Practice, vol. 12, no. 5, pp. 257–261, 2019.E. I. Boychuk, “Lexical and Grammatical Features of Internet Reviews in the Russian and English Languages,” Verhnevolzhski Philological Bulletin, no. 3 (26), pp. 107–115, 2021.E. I. Boychuk, “Lexical and Grammatical Features of Internet Reviews in the Russian and English Languages,” Verhnevolzhski Philological Bulletin, no. 3 (26), pp. 107–115, 2021.A. Y. Poletaev and I. V. Paramonov, “Recursive sentiment detection algorithm for Russian sentences,” Automatic Control and Computer Sciences, vol. 57, no. 7, pp. 740–749, 2023.A. Y. Poletaev and I. V. Paramonov, “Recursive sentiment detection algorithm for Russian sentences,” Automatic Control and Computer Sciences, vol. 57, no. 7, pp. 740–749, 2023.M. A. Eremina, “Rechevoj zhanr otzyva v kommunikativnom prostranstve interneta,” Nauchnyj dialog, no. 5 (53), pp. 34–45, 2016.M. A. Eremina, “Rechevoj zhanr otzyva v kommunikativnom prostranstve interneta,” Nauchnyj dialog, no. 5 (53), pp. 34–45, 2016.A. R. Kalashnikova, “Informativnaya Tekstovaya Tonal'nost' Kak Opredelyayushchij Faktor Ritmicheskoj Tekstovoj Organizacii,” Izvestiya Volgogradskogo Gosudarstvennogo Pedagogicheskogo Universiteta, vol. 3 (107), pp. 113–116, 2016.A. R. Kalashnikova, “Informativnaya Tekstovaya Tonal'nost' Kak Opredelyayushchij Faktor Ritmicheskoj Tekstovoj Organizacii,” Izvestiya Volgogradskogo Gosudarstvennogo Pedagogicheskogo Universiteta, vol. 3 (107), pp. 113–116, 2016.I. V. Paramonov and A. Y. Poletaev, “Annotation of Text Corpora by Sentiment and Presence of Irony within a Project of Citizen Science,” Modelirovanie i Analiz Informatsionnykh Sistem, vol. 30, no. 1, pp. 86–100, 2023.I. V. Paramonov and A. Y. Poletaev, “Annotation of Text Corpora by Sentiment and Presence of Irony within a Project of Citizen Science,” Modelirovanie i Analiz Informatsionnykh Sistem, vol. 30, no. 1, pp. 86–100, 2023.N. Loukachevitch and A. Levchik, “Creating a General Russian Sentiment Lexicon,” in Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), 2016, pp. 1171–1176.N. Loukachevitch and A. Levchik, “Creating a General Russian Sentiment Lexicon,” in Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), 2016, pp. 1171–1176.D. Kulagin, “Publicly Available Sentiment Dictionary for the Russian Language KartaSlovSent,” in Computational Linguistics and Intellectual Technologies: Proceesings of the Annual “Dialog” Conference (2021), 2021, pp. 1106–1119.D. Kulagin, “Publicly Available Sentiment Dictionary for the Russian Language KartaSlovSent,” in Computational Linguistics and Intellectual Technologies: Proceesings of the Annual “Dialog” Conference (2021), 2021, pp. 1106–1119.A. Y. Poletaev, I. V. Paramonov, and E. I. Boychuk, “Algorithm of Constituency Tree from Depencency Tree Construction for a Russian-Language Sentence,” Informatics and Automation, vol. 22, no. 6, pp. 1323–1353, 2023.A. Y. Poletaev, I. V. Paramonov, and E. I. Boychuk, “Algorithm of Constituency Tree from Depencency Tree Construction for a Russian-Language Sentence,” Informatics and Automation, vol. 22, no. 6, pp. 1323–1353, 2023.L. Breiman, J. Friedman, R. Olshen, and C. Stone, Classification and Regression Trees. Routledge, 2017.L. Breiman, J. Friedman, R. Olshen, and C. Stone, Classification and Regression Trees. Routledge, 2017.O. Koltsova, S. Alexeeva, S. Pashakhin, and S. Koltsov, “PolSentiLex: Sentiment Detection in Socio-Political Discussions on Russian Social Media,” in Conference on Artificial Intelligence and Natural Language, 2020, pp. 1–16.O. Koltsova, S. Alexeeva, S. Pashakhin, and S. Koltsov, “PolSentiLex: Sentiment Detection in Socio-Political Discussions on Russian Social Media,” in Conference on Artificial Intelligence and Natural Language, 2020, pp. 1–16.W. Souma, I. Vodenska, and H. Aoyama, “Enhanced News Sentiment Analysis Using Deep Learning Methods,” Journal of Computational Social Science, vol. 2, no. 1, pp. 33–46, 2019.W. Souma, I. Vodenska, and H. Aoyama, “Enhanced News Sentiment Analysis Using Deep Learning Methods,” Journal of Computational Social Science, vol. 2, no. 1, pp. 33–46, 2019.A. B. Junior, N. F. F. da Silva, T. C. Rosa, and C. G. C. Junior, “Sentiment Analysis with Genetic Programming,” Information Sciences, vol. 562, pp. 116–135, 2021.A. B. Junior, N. F. F. da Silva, T. C. Rosa, and C. G. C. Junior, “Sentiment Analysis with Genetic Programming,” Information Sciences, vol. 562, pp. 116–135, 2021.

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