Recursive sentiment detection algorithm for Russian sentences

A. Yu. Poletaev ; I. V. Paramonov

Modelirovanie i analiz informacionnyh sistem, Tome 29 (2022) no. 2, pp. 134-147.

Résumé

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.

Détail

Keywords: sentiment analysis, semantic rules
Mots-clés : sentiment detection, sentiment corpus.

Bibliographie

[1] I. Paramonov and A. Poletaev, “Adaptation of Semantic Rule-Based Sentiment Analysis Approach for Russian Language”, Proceedings of 30th Conference of Open Innovations Association FRUCT, 2021, 155–164 | DOI

[2] T. Wilson, J. Wiebe, and P. Hoffmann, “Recognizing contextual polarity in phrase-level sentiment analysis”, Proceedings of human language technology conference and conference on empirical methods in natural language processing, 2005, 347–354

[3] L. K.-W. Tan, J.-C. Na, Y.-L. Theng, and K. Chang, “Sentence-level sentiment polarity classification using a linguistic approach”, Proceedings ofInternational Conference on Asian Digital Libraries, 2011, 77–87

[4] Y. Xie, Z. Chen, K. Zhang, Y. Cheng, D. K. Honbo, A. Agrawal, and A. N. Choudhary, “MuSES: multilingual sentiment elicitation system for social media data”, IEEE Intelligent Systems, 29:4 (2014), 34–42 | DOI

[5] S. Smetanin and M. Komarov, “Deep transfer learning baselines for sentiment analysis in Russian”, Information Processing Management, 58:3 (2021), 102484 | DOI

[6] M. A. M. Shaikh, H. Prendinger, and M. Ishizuka, “Sentiment assessment of text by analyzing linguistic features and contextual valence assignment”, Applied Artificial Intelligence, 22:6 (2008), 558–601 | DOI

[7] O. Appel, F. Chiclana, J. Carter, and H. Fujita, “A hybrid approach to the sentiment analysis problem at the sentence level”, Knowledge-Based Systems, 108 (2016), 110–124 | DOI

[8] S. Kahane and N. Mazziotta, “Syntactic Polygraphs. A Formalism Extending Both Constituency and Dependency”, Proceedings of the 14th Meeting on the Mathematics of Language 2015 Recursive Sentiment Detection Algorithm for Russian Sentences, 2015, 152–164 | MR | Zbl

[9] Y. Gao, J.-G. Lou, and D. Zhang, A Hybrid Semantic Parsing Approach for Tabular Data Analysis, 2019, arXiv: 1910.10363v2 [cs.AI]

[10] J. Li, H. Tan, and M. Bansal, “Improving Cross-Modal Alignment in Vision Language Navigation via Syntactic Information”, Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021, 1041–1050

[11] Z. Marji, A. Nighojkar, and J. Licato, “Probing the Natural Language Inference Task with Automated Reasoning Tools”, The Thirty-Third International Flairs Conference, 2020

[12] R. Socher, A. Perelygin, J. Wu, J. Chuang, C. D. Manning, A. Y. Ng, and C. Potts, “Recursive deep models for semantic compositionality over a sentiment treebank”, Proceedings of the 2013 conference on empirical methods in natural language processing, 2013, 1631–1642

[13] K. S. Tai, R. Socher, and C. D. Manning, “Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks”, Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, v. 1, Long Papers, 2015

[14] Y. Zhang and Y. Zhang, “Tree communication models for sentiment analysis”, Proceedings of the 57th annual meeting of the association for computational linguistics, 2019, 3518–3527 | DOI

[15] D. Yin, T. Meng, and K.-W. Chang, “SentiBERT: A Transferable Transformer-Based Architecture for Compositional Sentiment Semantics”, Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 2020, 3695–3706 | DOI

[16] N. V. Loukachevitch and A. V. Levchick, “Creating a General Russian Sentiment Lexicon”, Proceedings of the Tenth International Conference on Language Resources and Evaluation, LREC'16, 2016, 1171–1176