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    Academic Journal

    Source: Discourse; Том 11, № 4 (2025); 121-138 ; Дискурс; Том 11, № 4 (2025); 121-138 ; 2658-7777 ; 2412-8562 ; 10.32603/2412-8562-2025-11-4

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    Relation: https://discourse.elpub.ru/jour/article/view/826/751; Popova E. O., Volkova Y. A. Identification of Extremism Signs through the Analysis of the Text Tonality // Functional Aspects of Intercultural Communication. Translation and Interpreting Issues. Vol. 6: XI Int. Sci. Interdisciplinary Conf. on Research and Methodology, Moscow, 24 Nov. 2023 / RUDN Univ. Moscow, 2023. С. 66–76. DOI:10.22363/2712-7974-2019-6-66-76.; Analysis of Tonality of Text Using Machine Learning / D. Gautham Sai, Govind Reddy S, D. Greeshma et al. // IJRASET. 2023. Vol. 11, iss. XII. P. 973–979. DOI:10.22214/ijraset.2023.57492.; Baydogan C., Alatas B. Sentiment analysis using Konstanz Information Miner in social networks // 6th Int. Symposium on Digital Forensic and Security (ISDFS), Antalya, 22–25 March 2018 / IEEE. Antalya, 2018. DOI:10.1109/ISDFS.2018.8355395.; Taboada M. Sentiment Analysis: An Overview from Linguistics // Annual Review of Linguistics. 2016. Vol. 2. P. 325–347. DOI:10.1146/annurev-linguistics-011415-040518.; Benamara F., Taboada M., Mathieu Y. Evaluative Language Beyond Bags of Words: Linguistic Insights and Computational Applications // Computational Linguistics. 2017. Vol. 43, № 1. С. 201–264. DOI:10.1162/COLI_a_00278.; A Meta-Framework for Modeling the Human Reading Process in Sentiment Analysis / R. Baly, R. Hobeica, H. Hajj et al. // ACM Transactions on Information Systems. 2017. Vol. 35, iss. 1: 7. DOI:10.1145/2950050.; Lu B. On computing textual sentiment with linguistic knowledge and semi-supervised learning: Dr. Sci. (Philosophy) Thesis / Hong Kong. City Univ. of Hong Kong, 2013.; Stepanov Ju. S. Some Burning Issues of Contemporary Semiotics // Linguistics. 1974. Vol. 12, iss. 141. P. 53–66. DOI:10.1515/ling.1974.12.141.53.; Veron E. Ideology and Social Sciences: A Communicational Approach // Semiotica. 1971. Т. 3, iss. 1. P. 59–76. DOI: https://doi.org/10.1515/semi.1971.3.1.59.; Allwood J. A Bird’s Eye View of Pragmatics // Papers from the Fourth Scandinavian Conference of Linguistics. Odense: Odense Univ. Press, 1978. P. 145–159.; Гриневич О. А. Динамика функционирования усадебного сверхтекста русской литературы: семантика, синтактика, прагматика // Известия Смолен. гос. ун-та. 2020. № 1 (49). С. 46–60. DOI:10.35785/2072-9464-2020-49-1-46-60.; Hogenboom A. Sentiment Analysis of Text Guided by Semantics and Structure. Rotterdam: Erasmus Univ. Rotterdam, 2009.; A New Approach for Carrying Out Sentiment Analysis of Social Media Comments Using Natural Language Processing / M. Ranjan, S. Tiwari, A. Md Sattar, N. S. Tatkar // Engineering Proceedings. 2023. Vol. 59, iss. 1: 181. DOI:10.3390/engproc2023059181.; Vilares D. Sentiment analysis for reviews and microtexts based on lexico-syntactic knowledge. 2013. URL: https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/FDIA2013.8 (дата обращения: 27.01.2025).; Chauhan D., Sutaria K., Doshi R. Impact of Semiotics on Multidimensional Sentiment Analysis on Twitter: A Survey // Second Int. Conf. on Computing Methodologies and Communication (ICCMC), 15–16 Feb. 2018 / Erode. IEEE, 2018. P. 671–674. DOI:10.1109/ICCMC.2018.8487851.; Liebmann M., Hagenau M., Neumann D. Information Processing in Electronic Markets: Measuring Subjective Interpretation Using Sentiment Analysis // ICIS 2012 Proceedings. 2012. URL: https://aisel.aisnet.org/icis2012/proceedings/KnowledgeManagement/9 (дата обращения: 27.01.2025).; Singh B., Kushwaha N., Vyas O. P. An interpretation of sentiment analysis for enrichment of Business Intelligence // IEEE Region 10 Conference (TENCON), Singapore, 22–25 Nov. 2016 / Singapore, IEEE, 2016. P. 18–23. DOI:10.1109/TENCON.2016.7847950.; Thiel K. Introduction to the KNIME Text Processing Extension // Text Mining and Visualization: Case Studies Using Open-Source Tools. 1st ed. / ed. by M. Hofmann, A. Chisholm. London; NY: Chapman and Hall, 2016. P. 63–80. DOI: https://doi.org/10.1201/b19007.; Meinl T., Jagla B., Berthold M. R. Integrated data analysis with KNIME // Open Source Software in Life Science Research / ed by L. Harland, M. Forster. Cambridge: Woodhead Publishing Ltd., 2012. pp. 151–171. DOI: https://doi.org/10.1533/9781908818249.151.; Dorr R. A., Casal J. J., Toriano R. Text Mining of Biomedical Articles Using the Konstanz Information Miner (KNIME) Platform: Hemolytic Uremic Syndrome as a Case Study // Healthcare Informatics Research. 2022. Vol. 28, no. 3. P. 276–283. DOI:10.4258/hir.2022.28.3.276.; Towards Simplification of Analytical Workflows With Semantics at Siemens (Extended Abstract) / E. Kharlamov, G. Mehdi, O. Savkovic et al. // IEEE Int. Conf. on Big Data (Big Data), Seattle, WA,10–13 Dec. 2018 / IEEE. Seattle, 2018. P. 1951–1954. DOI:10.1109/BigData.2018.8622652.; Smart Technologies for Genre Closeness Evaluation / E. Isaeva, O. Mnazhula, O. Baiburova, R. Crawford // Lecture Notes in Networks and Systems. 2022. Vol. 342, Cham: Springer. DOI:10.1007/978-3-030-89477-1_60.; Isaeva E. Topic Modelling in Computer Security Discourse: a Case Study of Whitepaper Publications and News Feeds // Perm Univ. Herald. Russian and Foreign Philology. 2022. Vol. 14, iss. 2. P. 18–26. DOI:10.17072/2073-6681-2022-2-18-26.; Valtolina S., Barricelli B. R., Dittrich Y. Participatory knowledge-management design: A semiotic approach // J. of Visual Languages & Computing. 2012. Vol. 23, iss. 2. P. 103–115. DOI: https://doi.org/10.1016/j.jvlc.2011.11.007.; 03_Sentiment_Classification rev 1 – KNIME Community Hub // KNIME Open for Innovation. 23.06.2024. URL: https://hub.knime.com/rfeigel/spaces/Public/03_Sentiment_Classification%20rev%201~4i6l8oqEGQ_ngBU5/current-state (дата обращения: 27.01.2025).; https://discourse.elpub.ru/jour/article/view/826

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    Academic Journal

    Source: Foreign Languages in Higher Education; Vol. 19 No. 2 (19) (2015); 42-52 ; Օտար լեզուները բարձրագույն դպրոցում; Vol. 19 No. 2 (19) (2015); 42-52 ; Иностранные языки в высшей школе; Том 19 № 2 (19) (2015); 42-52 ; 2953-8149 ; 1829-2453 ; 10.46991/FLHE/2015.19.2

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