The design of the algorithm for opinion mining in the media discourse in English and Belarusian

Authors

  • Veranika V. Kazlova Minsk State Linguistic University, 21 Zacharava Street, Minsk 220034, Belarus

Keywords:

algorithm, opinion mining, vocabulary of linguistic markers, media discourse, automatic classification of opinions
Supporting Agencies
The author wishes to present her gratitude to  T. P. Karpilovich , doctor of science (philology), full professor.

Abstract

The article runs about the design of the algorithm for opinion mining in the media discourse in English and Belarusian. The algorithm is based on the vocabularies of linguistic units that mark opinions in the media discourse (733 units that are mentioned in more than 5000 opinionated contexts). The advantage of the proposed model lies in the fact that it considers the semantical meaning of the linguistic markers in both languages, their frequency, and the typology of opinionated passages in the context of automatic sorting of opinions. The algorithm includes a number of steps: the search of opinion markers based on the developed vocabularies of linguistic markers, the assignment of a certain «weight» to each sentence depending on the markers (by the sum of numeric values from the vocabulary; the article provides data on the «weight» of linguistic markers in a generalized form), then statements are divided into opinions-interpretations, opinions-theories, opinions-assumptions and an opinion’s combined type. The classification takes into account semantic and quantitative indicators.

Author Biography

  • Veranika V. Kazlova, Minsk State Linguistic University, 21 Zacharava Street, Minsk 220034, Belarus

    postgraduate student at the department of English theory and practice, faculty of translation and interpretation.

References

  1. Dobrosklonskaya TG. Medialingvistika: sistemnyi podkhod k izucheniyu yazyka SMI: sovremennaya angliiskaya mediarech’ [Media linguistics: system approach to the media language studies: modern English media speech]. Moscow: Flinta; 2008. 263 p. Russian.
  2. Krasnoyarova OV. Media text: its characteristics and types. Izvestiya Irkutskoi gosudarstvennoi ekonomicheskoi akademii. 2010;3:177–181. Russian.
  3. Tyrygina VA. Zhanrovaya stratifikatsiya mass-mediinogo diskursa [Genre stratification of the mass media discourse]. Moscow: Librolom; 2010. 320 p. Russian.
  4. Ivchenkov VI. Lingvisticheskaya organizatsiya teksta: v tvorcheskoi laboratorii Vladimira Korotkevicha [Linguistic organization of the text: in the creative laboratory of Vladimir Korotkevich]. Minsk: Belarusian State University; 2002. 211 p. Russian.
  5. Ivchenkov VI. [Journalism in the cultural system: media centricity of the modern world]. In: Lokotko AI, editor. Pershy mizhnarodny navukovy kangrjes belaruskaj kul’tury. Zbornik matjeryjalaw; 5–6 maja 2016 g; Minsk, Belarus’ [The first international scientific congress of the Belarusian culture: collection of materials; 2016 May 5–6; Minsk, Belarus]. Minsk: Pravo i ekonomika; 2016. p. 278–282. Russian.
  6. Zaliznyak AA. Mnogoznachnost’ v yazyke i sposoby ee predstavleniya [Polysemy in the language and the means of its representation]. Moscow: Yazyki slavyanskikh kul’tur; 2006. 672 p. Russian.
  7. Annenkova IV. [The language of the modern mass media as the system of interpretation in the context of Russian culture]. In: Solganic GYa, editor. Yazyk sovremennoi publitsistiki [The language of modern journalism]. Moscow: Flinta; 2007. p. 99–114. Russian.
  8. Bing Liu. Sentiment Analysis and Opinion Mining. California: Morgan & Claypool Publishers; 2012. 168 p.
  9. Pang Bo. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval. 2008;2(1–2):1–135. DOI: 10/1561/150000001.
  10. Turney P. Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings 40th annual meeting of the association for computational linguistics. Philadelphia: Association for Computer Linguistics; 2002. p. 417–424. DOI: 10.3115/1073083.1073153.
  11. Periakaruppan S, Shanmugasundaram H, Joan L. Research directions, challenges and issues in opinion mining. International Journal of Advanced Science and Technology. 2013;60:1–8. DOI:10.14257/ijast.2013.60.01.
  12. Kotelnikov EV, Klekovkina MV. Sentiment analysis of texts based on machine learning methods. Komp’yuternaya lingvistika i intellektual’nye tekhnologii. 2012;11:27–36. Russian
  13. Bing Liu. Web data mining. Luxembourg: Springer Science + Business Media; 2011. 622 p.
  14. Tsytsarau M, Palpanas T. Survey on mining subjective data on the web. Data Mining and Knowledge Discovery. 2012;24(3): 478–514. DOI: 10.1007/s10618-011-0238-6.
  15. Edwards D. Newspeak in the 21st century. London: Pluto Press; 2009. 304 p.
  16. Simonov AK, Gorbanevskij MV. Ponyatie chesti, dostoinstva i delovoi reputatsii: spornye teksty SMI i problemy ikh analiza i otsenki yuristami i lingvistami [The concept of honor, dignity and business reputation: controversial media texts and problems of their analysis and evaluation by lawyers and linguists]. Moscow: Medeya; 2004. 328 p. Russian.
  17. Soloshenko N, Orlova YA, Rosaliev VL. Automatic allocation of subjects and themes from the stream of news reports. Izvestiya Volgogradskogo gosudarstvennogo tekhnicheskogo universiteta. 2015;2:83–90. Russian.
  18. Gibatova GF. Truth in opinion contexts. Vestnik Yuzhno-Ural’skogo gosudarstvennogo universiteta. 2011;22:54–58. Russian.
  19. Ivanova GF. On opinions and evaluation. Izvestia: Herzen University Journal of Humanities & Sciences. 2007;8:25–31. Russian.
  20. Dmitrovskaya MA. [Knowledge and opinion: the image of the world, the image of a person]. In: Arutyanova ND, editor. Logicheskii analiz yazyka. Znanie i mnenie [Logical analysis of the language, knowledge and opinion]. Moscow: Nauka; 1998. p. 6–17. Russian.
  21. Goroshko EI. Yazykovoe soznanie: gendernaya paradigma [Language consciousness: a gender paradigm]. Kharkov: ID INJEK; 2003. 437 p. Russian.
  22. Hai-Jew S, editor. Data analytics in digital humanities, multimedia systems and applications. Manhattan: Springer Science; 2017. 317 p.
  23. Pouliquen B, Steinberger R, Best C. Automatic detection of quotations in multilingual news. In: Proceedings of the International Conference Recent Advances in Natural Language Processing (RANLP’2007). Bulgaria: Borovets; 2007. p. 487–492.
  24. Clergerie É. L, Sagot B, Stern R, Denis P, Recourcé G, Mignot V. Extracting and visualizing quotations from news wires. In: Vetulani Z, editor. Human language technology. Challenges for computer science and linguistics, lecture notes in computer science. Berlin: Springer; 2011. p. 522–532.

Downloads

Published

2020-12-11

Issue

No. 3 (2020): Journal of the Belarusian State University. Philology

Section

Linguistics

License

The authors who are published in this journal agree to the following:

  1. The authors retain copyright on the work and provide the journal with the right of first publication of the work on condition of license Creative Commons Attribution-NonCommercial. 4.0 International (CC BY-NC 4.0).
  2. The authors retain the right to enter into certain contractual agreements relating to the non-exclusive distribution of the published version of the work (e.g. post it on the institutional repository, publication in the book), with the reference to its original publication in this journal.
  3. The authors have the right to post their work on the Internet (e.g. on the institutional store or personal website) prior to and during the review process, conducted by the journal, as this may lead to a productive discussion and a large number of references to this work. (See The Effect of Open Access.)

How to Cite

[1]
Kazlova, V.V. 2020. The design of the algorithm for opinion mining in the media discourse in English and Belarusian. Journal of the Belarusian State University. Philology. 3 (Dec. 2020), 40–49.