Adaptation of sentiment analysis techniques to Persian language

Kia Dashtipour, Amir Hussain, Alexander Gelbukh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

© Springer Nature Switzerland AG 2018. In the recent years, people all around the world share their opinions about different fields with each other over Internet. Sentiment analysis techniques have been introduced to classify these rich data based on the polarity of the opinion. Sentiment analysis research has been growing rapidly; however, most of the research papers are focused on English. In this paper, we review English-based sentiment analysis approaches and discuss what adaption these approaches require to become applicable to the Persian language. The results show that approaches initially suggested for English language are competitive with those developed specifically for Persian sentiment analysis.
Original languageAmerican English
Title of host publicationAdaptation of sentiment analysis techniques to Persian language
Pages129-140
Number of pages114
ISBN (Electronic)9783319771151
DOIs
StatePublished - 1 Jan 2018
EventLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) -
Duration: 1 Jan 2019 → …

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10762 LNCS
ISSN (Print)0302-9743

Conference

ConferenceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Period1/01/19 → …

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  • Cite this

    Dashtipour, K., Hussain, A., & Gelbukh, A. (2018). Adaptation of sentiment analysis techniques to Persian language. In Adaptation of sentiment analysis techniques to Persian language (pp. 129-140). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10762 LNCS). https://doi.org/10.1007/978-3-319-77116-8_10