Building the Semantic Similarity Model for Social Network Data Streams

Svitlana Petrasova , Nina Khairova , Włodzimierz Lewoniewski

Abstract

This paper proposes the model for searching similar collocations in English texts in order to determine semantically connected text fragments for social network data streams analysis. The logical-linguistic model uses semantic and grammatical features of words to obtain a sequence of semantically related to each other text fragments from different actors of a social network. In order to implement the model, we leverage Universal Dependencies parser and Natural Language Toolkit with the lexical database WordNet. Based on the Blog Authorship Corpus, the experiment achieves over 0.92 precision.
Author Svitlana Petrasova - National Technical University, Kharkiv Polytechnic Institute, Kharkiv, Ukraine
Svitlana Petrasova,,
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, Nina Khairova - National Technical University, Kharkiv Polytechnic Institute, Kharkiv, Ukraine
Nina Khairova,,
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, Włodzimierz Lewoniewski (WIiGE / KIE)
Włodzimierz Lewoniewski,,
- Department of Information Systems
Pages21-24
Publication size in sheets0.5
Book Vynokurova Olena, Peleshko Dmytro (eds.): Proceedings of the 2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP), 2018, Institute of Electrical and Electronics Engineers, ISBN 978-1-5386-2874-4, 598 p.
Keywords in Englishsocial network; data stream; collocations; semantic similarity; blogs; corpus; Universal Dependencies; WordNet
DOIDOI:10.1109/DSMP.2018.8478480
URL https://ieeexplore.ieee.org/abstract/document/8478480/
Languageen angielski
Score (nominal)20
Score sourcepublisherList
ScoreMinisterial score = 20.0, 11-09-2020, ChapterFromConference
Publication indicators WoS Citations = 1
Citation count*5 (2020-09-28)
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* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.
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