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Mass Media Evaluation Using Topic Modelling

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Digital Transformation and Global Society (DTGS 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1242))

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Abstract

Automatic evaluation of public opinion is an actual problem in many areas, including both governmental and private sectors. There is number of scientific schools and corporations which work on to solve the problem of automatic evaluation of publications in media, social networks and other internet resources, in order to solve such problems as evaluating public image of a company, product or persona, evaluating work of PR departments and agencies, analyzing the most socially significant and resonant newsmakers and issues. The problems involve area of natural language processing and understanding, which is considered to be technologically and mathematically complex, and is nowadays being solved using deep learning models, which require a large marked dataset with texts of similar domain, which is hard and expensive to obtain. Another problem of such systems is performance issues. In this work an informational system is described, which attempts to solve the outlined problems. In the paper an approach is proposed, which allows to classify the most important/positive/negative/resonant topics and publications, and to analyze their dynamic characteristics. The proposed approach is not based on manual creation of keyword dictionary, or labelling of big amounts of documents and allows to evaluate documents according to arbitrary criterion. The approach was verified on one criterion by comparing it’s results to a dictionary-based system.

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Author information

Authors and Affiliations

  1. Satbayev University, 22A Satbayev St., Almaty, Kazakhstan

    Kirill Yakunin, Ravil Mukhamediev, Sanzhar Murzakhmetov & Rassul Yunussov

  2. ISMA University, 1 Lomonosov St., Riga, Latvia

    Ravil Mukhamediev

  3. Institute of Information and Computational Technologies, Pushkin St., Almaty, Kazakhstan

    Kirill Yakunin, Ravil Mukhamediev, Rustam Mussabayev, Yan Kuchin & Sanzhar Murzakhmetov

  4. Information-Analytical Center, 18 Dostyk St., Nur-Sultan, Kazakhstan

    Timur Buldybayev & Ulzhan Ospanova

Authors
  1. Kirill Yakunin
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  2. Ravil Mukhamediev
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  3. Rustam Mussabayev
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  4. Timur Buldybayev
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  5. Yan Kuchin
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  6. Sanzhar Murzakhmetov
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  7. Rassul Yunussov
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  8. Ulzhan Ospanova
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Corresponding author

Correspondence to Ravil Mukhamediev .

Editor information

Editors and Affiliations

  1. National Research University Higher School of Economics, St. Petersburg, Russia

    Daniel A. Alexandrov

  2. ITMO University, St. Petersburg, Russia

    Alexander V. Boukhanovsky

  3. ITMO University, St. Petersburg, Russia

    Andrei V. Chugunov

  4. National Research University Higher School of Economics, St. Petersburg, Russia

    Yury Kabanov

  5. National Research University Higher School of Economics, St. Petersburg, Russia

    Olessia Koltsova

  6. National Research University Higher School of Economics, St. Petersburg, Russia

    Ilya Musabirov

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Yakunin, K. et al. (2020). Mass Media Evaluation Using Topic Modelling. In: Alexandrov, D.A., Boukhanovsky, A.V., Chugunov, A.V., Kabanov, Y., Koltsova, O., Musabirov, I. (eds) Digital Transformation and Global Society. DTGS 2020. Communications in Computer and Information Science, vol 1242. Springer, Cham. https://doi.org/10.1007/978-3-030-65218-0_13

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  • DOI: https://doi.org/10.1007/978-3-030-65218-0_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-65217-3

  • Online ISBN: 978-3-030-65218-0

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