Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
Social Network Sentiment Analysis and Message Clustering. / Kharlamov, Alexander A. ; Orekhov, Andrey V. ; Bodrunova, Svetlana S. ; Lyudkevich, Nikolay S. .
Internet Science. 6th International Conference, INSCI 2019 : Proceedings. ed. / Samira El Yacoubi; Franco Bagnoli; Giovanna Pacini. Cham : Springer Nature, 2019. p. 18-31 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11938 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
}
TY - GEN
T1 - Social Network Sentiment Analysis and Message Clustering
AU - Kharlamov, Alexander A.
AU - Orekhov, Andrey V.
AU - Bodrunova, Svetlana S.
AU - Lyudkevich, Nikolay S.
PY - 2019/12/1
Y1 - 2019/12/1
N2 - 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.
AB - 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.
KW - Approximation-estimation test
KW - Lexical mask
KW - Markov moment
KW - Message clustering
KW - Sentiment analysis
KW - Short texts
KW - Tonality
KW - Twitter
UR - http://www.scopus.com/inward/record.url?scp=85076575688&partnerID=8YFLogxK
UR - http://www.mendeley.com/research/social-network-sentiment-analysis-message-clustering
U2 - 10.1007/978-3-030-34770-3_2
DO - 10.1007/978-3-030-34770-3_2
M3 - Conference contribution
AN - SCOPUS:85076575688
SN - 9783030347697
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 18
EP - 31
BT - Internet Science. 6th International Conference, INSCI 2019
A2 - El Yacoubi, Samira
A2 - Bagnoli, Franco
A2 - Pacini, Giovanna
PB - Springer Nature
CY - Cham
T2 - 6th International Conference on Internet Science, INSCI 2019
Y2 - 2 December 2019 through 5 December 2019
ER -
ID: 49784768