Social Network Sentiment Analysis and Message Clustering

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

5 Scopus citations

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.

Original languageEnglish
Title of host publicationInternet Science. 6th International Conference, INSCI 2019
Subtitle of host publicationProceedings
EditorsSamira El Yacoubi, Franco Bagnoli, Giovanna Pacini
Place of PublicationCham
PublisherSpringer Nature
Pages18-31
Number of pages14
ISBN (Electronic)9783030347703
ISBN (Print)9783030347697
DOIs
StatePublished - 1 Dec 2019
Event6th International Conference on Internet Science, INSCI 2019 - Perpignan, France
Duration: 2 Dec 20195 Dec 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11938 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th International Conference on Internet Science, INSCI 2019
Abbreviated titleINSCI'2019
CountryFrance
CityPerpignan
Period2/12/195/12/19

Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Keywords

  • Approximation-estimation test
  • Lexical mask
  • Markov moment
  • Message clustering
  • Sentiment analysis
  • Short texts
  • Tonality
  • Twitter

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