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International Conference on Internet Science

INSCI 2019: Internet Science pp 18–31Cite as

Social Network Sentiment Analysis and Message Clustering

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11938))

Abstract

Till today, classification of documents into negative, neutral, or positive remains a key task within the analysis of text tonality/sentiment. There are several methods for the automatic analysis of text sentiment. The method based on network models, the most linguistically sound, to our viewpoint, allows us take into account the syntagmatic connections of words. Also, it utilizes the assumption that not all words in a text are equivalent; some words have more weight and cast higher impact upon the tonality of the text than others. We see it natural to represent a text as a network for sentiment studies, especially in the case of short texts where grammar structures play a higher role in formation of the text pragmatics and the text cannot be seen as just “a bag of words”. We propose a method of text analysis that combines using a lexical mask and an efficient clustering mechanism. In this case, cluster analysis is one of the main methods of typology which demands obtaining formal rules for calculating the number of clusters. The choice of a set of clusters and the moment of completion of the clustering algorithm depend on each other. We show that cluster analysis of data from an n-dimensional vector space using the “single linkage” method can be considered a discrete random process. Sequences of “minimum distances” define the trajectories of this process. “Approximation-estimating test” allows establishing the Markov moment of the completion of the agglomerative clustering process.

This research in the part of data interpretation and literature review has been supported by the Russian Science Foundation grant 16-18-10125-P (2016–2018, prolongued for 2019–2020).

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Acknowledgements

This research in the part of data interpretation and literature review has been supported by the Russian Science Foundation grant 16-18-10125-P (2016–2018, prolongued for 2019–2020).

Author information

Authors and Affiliations

  1. Institute of Higher Nervous Activity and Neurophysiology of Russian Academy of Sciences, 5A Butlerova street, Moscow, 117485, Russian Federation

    Alexander A. Kharlamov

  2. Moscow State Linguistic University, 38 Ostozhenka street, Moscow, 119034, Russian Federation

    Alexander A. Kharlamov

  3. Higher School of Economics, 20 Myasnitskaya street, Moscow, 101000, Russian Federation

    Alexander A. Kharlamov

  4. St. Petersburg State University, 7/9, Universitetskaya embankment, St.Petersburg, 199034, Russian Federation

    Andrey V. Orekhov, Svetlana S. Bodrunova & Nikolay S. Lyudkevich

Authors
  1. Alexander A. Kharlamov
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  2. Andrey V. Orekhov
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  3. Svetlana S. Bodrunova
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  4. Nikolay S. Lyudkevich
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Corresponding author

Correspondence to Andrey V. Orekhov .

Editor information

Editors and Affiliations

  1. University of Perpignan Via Domitia, Perpignan Cedex 9, France

    Samira El Yacoubi

  2. University of Florence, Sesto Fiorentino, Italy

    Franco Bagnoli

  3. University of Florence, Sesto Fiorentino, Italy

    Giovanna Pacini

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Kharlamov, A.A., Orekhov, A.V., Bodrunova, S.S., Lyudkevich, N.S. (2019). Social Network Sentiment Analysis and Message Clustering. In: El Yacoubi, S., Bagnoli, F., Pacini, G. (eds) Internet Science. INSCI 2019. Lecture Notes in Computer Science(), vol 11938. Springer, Cham. https://doi.org/10.1007/978-3-030-34770-3_2

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  • DOI: https://doi.org/10.1007/978-3-030-34770-3_2

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  • Publisher Name: Springer, Cham

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