Social Network Sentiment Analysis and Message Clustering

Результат исследований: Публикации в книгах, отчётах, сборниках, трудах конференцийстатья в сборнике материалов конференциинаучнаярецензирование

6 Цитирования (Scopus)


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.

Язык оригиналаанглийский
Название основной публикацииInternet Science. 6th International Conference, INSCI 2019
Подзаголовок основной публикацииProceedings
РедакторыSamira El Yacoubi, Franco Bagnoli, Giovanna Pacini
Место публикацииCham
ИздательSpringer Nature
Число страниц14
ISBN (электронное издание)9783030347703
ISBN (печатное издание)9783030347697
СостояниеОпубликовано - 1 дек 2019
Событие6th International Conference on Internet Science (INSCI) 2019 - Perpignan, Франция
Продолжительность: 2 дек 20195 дек 2019

Серия публикаций

НазваниеLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Том11938 LNCS
ISSN (печатное издание)0302-9743
ISSN (электронное издание)1611-3349


конференция6th International Conference on Internet Science (INSCI) 2019
Сокращенный заголовокINSCI'2019

Предметные области Scopus

  • Теоретические компьютерные науки
  • Компьютерные науки (все)

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