Recursive Sentiment Detection Algorithm for Russian Sentences
-
Published:2023-12
Issue:7
Volume:57
Page:740-749
-
ISSN:0146-4116
-
Container-title:Automatic Control and Computer Sciences
-
language:en
-
Short-container-title:Aut. Control Comp. Sci.
Author:
Poletaev A. Y.ORCID, Paramonov I. V.ORCID
Reference16 articles.
1. Paramonov, I. and Poletaev, A., Adaptation of semantic rule-based sentiment analysis approach for russian language, 2021 30th Conf. of Open Innovations Association FRUCT, Oulu, Finland, 2021, IEEE, 2021, pp. 155–164. https://doi.org/10.23919/fruct53335.2021.9599992 2. Wilson, T., Wiebe, J., and Hoffmann, P., Recognizing contextual polarity in phrase-level sentiment analysis, Proc. Conf. on Human Language Technology and Empirical Methods in Natural Language Processing, Vancouver, 2005, Stroudsburg, Pa.: Association for Computational Linguistics, 2005, pp. 347–354. https://doi.org/10.3115/1220575.1220619 3. Kien-Weng Tan, L., Na, J.-Ch., Theng, Yi.-L., and Chang, K., Sentence-level sentiment polarity classification using a linguistic approach, Digital Libraries: For Cultural Heritage, Knowledge Dissemination, and Future Creation. ICADL 2011, Lecture Notes in Computer Sciences, vol. 7008, Berlin: Springer, 2011, pp. 77–87. https://doi.org/10.1007/978-3-642-24826-9_13 4. Xie, Yu., Chen, Z., Zhang, K., Cheng, Yu., Honbo, D., Agrawal, A., and Choudhary, A., MuSES: Multilingual sentiment elicitation system for social media data, IEEE Intell. Syst., 2013, vol. 29, no. 4, pp. 34–42. https://doi.org/10.1109/mis.2013.52 5. Smetanin, S. and Komarov, M., Deep transfer learning baselines for sentiment analysis in Russian, Inf. Process. Manage., 2021, vol. 58, no. 3, p. 102484. https://doi.org/10.1016/j.ipm.2020.102484
|
|