Abstract
The article deals with the development of an ontological model of words in public political discourse and texts of public speeches in the Kazakh language. The article presents an ontological model of the subject area of elections, a referendum, examples of processing queries from the knowledge base are given. A sentimental analysis of political discourse in social networks in the Kazakh language was carried out in order to determine the mood of the discussion in these sources.
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Bekmanova, G., Yelibayeva, G., Aubakirova, S., Dyussupova, N., Sharipbay, A., Nyazova, R.: Methods for analyzing polarity of the Kazakh texts related to the terrorist threats. In: Misra, S., et al. (eds.) ICCSA 2019. LNCS, vol. 11619, pp. 717–730. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-24289-3_53
Yergesh, B., Bekmanova, G., Sharipbay, A.: Sentiment analysis of Kazakh text and their polarity. Web Intell. 17(1), 9–15 (2019). https://doi.org/10.3233/WEB-190396
Bekmanova, G., Yergesh, B., Sharipbay, A.: Sentiment analysis model based on the word structural representation. In: Mahmud, M., Kaiser, M.S., Vassanelli, S., Dai, Q., Zhong, N. (eds.) BI 2021. LNCS (LNAI), vol. 12960, pp. 170–178. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86993-9_16
Bekmanova, G., Yergesh, B., Sharipbay, A., Mukanova, A.: Emotional speech recognition method based on word transcription. Sensors 22(5) (2022). https://doi.org/10.3390/s22051937
Yergesh, B., Bekmanova, G., Sharipbay, A.: Sentiment analysis on the hotel reviews in the Kazakh language. In: Paper Presented at the 2nd International Conference on Computer Science and Engineering, UBMK 2017, pp. 790–794 (2017). https://doi.org/10.1109/UBMK.2017.8093531
Yergesh, B., Bekmanova, G., Sharipbay, A., Yergesh, M.: Ontology-based sentiment analysis of Kazakh sentences. In: Gervasi, O., et al. (eds.) ICCSA 2017. LNCS, vol. 10406, pp. 669–677. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-62398-6_47
Zhetkenbay, L., Sharipbay, A., Bekmanova, G., Kamanur, U.: Ontological modeling of morphological rules for the adjectives in Kazakh and Turkish languages. J. Theor. Appl. Inf. Technol. 91(2), 257–263 (2016)
Bekmanova, G., et al.: A uniform morphological analyzer for the Kazakh and Turkish languages. In: Paper Presented at the CEUR Workshop Proceedings, pp. 20–30 (2017)
Raxmatovna, B.N.: Specific features of political speech. Central Asian J. Lit. Philos. Cult. 3(12), 80–87 (2022)
Tameryan, T.Yu., et al.: Political media communication: bilingual strategies in the pre-election campaign speeches. Online J. Commun. Media Technol. 9(4), e201921 (2019)
Al Maani, B., et al.: The positive-self and negative-other representation in Bashar Al-Assad’s first political speech after the Syrian uprising. Theory Pract. Lang. Stud. 12(10), 2201–2210 (2022)
Sotvoldiyevna, U.D.: Political Euphemisms in English and Uzbek languages (A comparative analysis). Eurasian J. Learn. Acad. Teach. 9, 92–96 (2022)
Dave, P.: Analysis of the political power speeches of Jr. Martin Luther King and Barrack Obama: in the light of critical discourse analysis as a literary research method. Vidhyayana-Int. Multi. Peer-Rev. E-Journal-ISSN 7(5), 2454–8596 (2022)
Abdurashetona, A.M., Ismailovich, I.O.: Methods of tagging part of speech of Uzbek language. In: 2021 6th International Conference on Computer Science and Engineering (UBMK). IEEE (2021)
Fiorelli, M., et al.: Metadata-driven semantic coordination. In: Garoufallou, E., Fallucchi, F., William De Luca, E. (eds.) MTSR 2019. CCIS, vol. 1057, pp. 16–27. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-36599-8_2
Langer, A.M.: Analysis and Design of Next-Generation Software Architectures. Springer, New York (2020). https://doi.org/10.1007/978-3-030-36899-9
Lai, C.: Fast retrieval algorithm of English sentences based on artificial intelligence machine translation. In: Atiquzzaman, M., Yen, N., Xu, Z. (eds.) 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, vol. 102. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-7466-2_117
Abdurashetona, A.M., Mokhiyakon, U.: Software features and linguistic features of Uzbek Synonymizer. In: 2022 7th International Conference on Computer Science and Engineering (UBMK). IEEE (2022)
Bekmanova, G., et al.: Linguistic foundations of low-resource languages for speech synthesis on the example of the Kazakh language. In: Gervasi, O., Murgante, B., Misra, S., Rocha, A.M.A.C., Garau, C. (eds.) Computational Science and Its Applications–ICCSA 2022 Workshops: Malaga, Spain, 4–7 July 2022, Proceedings, Part III. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-10545-6_1
Ibrahim, M.: A corpus-based comparative analysis of assertive strategies in Pakistani democratic and dictatorial speeches. J. Appl. Linguist. TESOL 5(4), 6–19 (2022)
Mohammed, T.A.S., Banda, F., Patel, M.: The Topoi of Mandela’s death in the Arabic speaking media: a corpus-based political discourse analysis (2022)
Liu, M.: Stancetaking in Hong Kong political discourse: a corpus-assisted discourse study. Chin. Lang. Discourse 13(1), 79–98 (2022)
Afzaal, M.: “Review of Literature.” A Corpus-Based Analysis of Discourses on the Belt and Road Initiative: Corpora and the Belt and Road Initiative, pp. 17–37. Springer, Singapore (2023)
Anand, S., Keefer, R.: From description to code: a method to predict maintenance codes from maintainer descriptions. Maintenance Reliab. Condition Monit. 2(2), 35–44 (2022)
Ma, Y., et al.: An end-to-end dialogue state tracking system with machine reading comprehension and wide & deep classification. arXiv preprint arXiv:1912.09297 (2019)
Saravanan, S., Sudha, K.: GPT-3 powered system for content generation and transformation. In: 2022 Fifth International Conference on Computational Intelligence and Communication Technologies (CCICT). IEEE (2022)
Dmytriv, A., et al.: Comparative analysis of using different parts of speech in the Ukrainian texts based on stylistic approach. In: CEUR Workshop Proceedings, vol. 3171 (2022)
Tretyakov, E., et al.: Sentiment analysis of social networks messages. In: Klimov, V.V., Kelley, D.J. (eds.) Biologically Inspired Cognitive Architectures 2021: Proceedings of the 12th Annual Meeting of the BICA Society. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-96993-6_61
Goswami, S., Hudnurkar, M., Ambekar, S.: Fake news and hate speech detection with machine learning and NLP. PalArch’s J. Archaeol. Egypt/Egyptol. 17(6), 4309–4322 (2020)
Lee, E., et al.: Racism detection by analyzing differential opinions through sentiment analysis of tweets using stacked ensemble GCR-NN model. IEEE Access 10, 9717–9728 (2022)
Alshalan, R., Al-Khalifa, H.: A deep learning approach for automatic hate speech detection in the Saudi Twittersphere. Appl. Sci. 10(23), 8614 (2020)
Chu, K.E., Keikhosrokiani, P., Asl, M.P.: A topic modeling and sentiment analysis model for detection and visualization of themes in literary texts. Pertanika J. Sci. Technol. 30(4), 2535–2561 (2022)
Babu, N.V., Kanaga, E.G.M.: Sentiment analysis in social media data for depression detection using artificial intelligence: a review. SN Comput. Sci. 3, 1–20 (2022)
Perifanos, K., Goutsos, D.: Multimodal hate speech detection in Greek social media. Multimodal Technol. Interact. 5(7), 34 (2021)
Aljarah, I., et al.: Intelligent detection of hate speech in Arabic social network: a machine learning approach. J. Inf. Sci. 47(4), 483–501 (2021)
Koltsova, O., et al.: PolSentiLex: sentiment detection in socio-political discussions on Russian social media. In: Filchenkov, A., Kauttonen, J., Pivovarova, L. (eds.) Artificial Intelligence and Natural Language: 9th Conference, AINL 2020, Helsinki, Finland, 7–9 October 2020, Proceedings. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59082-6_1
Mahmud, Md.A.I., et al.: Toward news authenticity: synthesizing natural language processing and human expert opinion to evaluate news. IEEE Access 11, 11405–11421 (2023)
Widodo, D.A., Iksan, N., Sunarko, B.: Sentiment analysis of Twitter media for public reaction identification on COVID-19 monitoring system using hybrid feature extraction method. Int. J. Intell. Syst. Appl. Eng. 11(1), 92–99 (2023)
Holt, K., Ustad Figenschou, T., Frischlich, L.: Key dimensions of alternative news media. Digital Journalism 7(7), 860–869 (2019). High-Choice Information Environments, vol. 25
Chang, W.-L., Tseng, H.-C.: The impact of sentiment on content post popularity through emoji and text on social platforms. In: Cyber Influence and Cognitive Threats, pp. 159–184. Academic Press (2020)
Dang, C.N., Moreno-García, M.N., De la Prieta, F.: An approach to integrating sentiment analysis into recommender systems. Sensors 21(16), 5666 (2021)
Wu, C., et al.: SentiRec: sentiment diversity-aware neural news recommendation. In: Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing (2020)
Rozado, D., Al-Gharbi, M., Halberstadt, J.: Prevalence of prejudice-denoting words in news media discourse: a chronological analysis. Soc. Sci. Comput. Rev. 08944393211031452 (2021)
Oladele, T.M., Ayetiran, E.F.: Social unrest prediction through sentiment analysis on Twitter using support vector machine: experimental study on Nigeria’s# EndSARS. Open Inf. Sci. 7(1), 20220141 (2023)
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Bekmanova, G., Yergesh, B., Ukenova, A., Omarbekova, A., Mukanova, A., Ongarbayev, Y. (2023). Sentiment Processing of Socio-political Discourse and Public Speeches. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2023 Workshops. ICCSA 2023. Lecture Notes in Computer Science, vol 14108. Springer, Cham. https://doi.org/10.1007/978-3-031-37117-2_15
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