Open AccessA Review on Negation Role in Twitter Sentiment Analysis
Publication year - 2021
Publication title -
international journal of healthcare information systems and informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.266
H-Index - 13
eISSN - 1555-340X
pISSN - 1555-3396
DOI - 10.4018/ijhisi.20211001oa06
Subject(s) - negation , polarity (international relations) , sentiment analysis , computer science , scope (computer science) , natural language processing , focus (optics) , field (mathematics) , artificial intelligence , phenomenon , social media , data science , linguistics , world wide web , epistemology , mathematics , programming language , philosophy , genetics , physics , cell , pure mathematics , optics , biology
Negation is an important linguistic phenomenon that needs to be considered for identifying correct sentiments from the opinionated data available in digital form. It has the power to alter the polarity or strength of the polarity of affected words. In this paper, the authors present a survey on the negation role that has been done until now in sentiment analysis, specifically Twitter sentiment analysis. The authors discuss the various approaches of modelling negation in Twitter sentiment analysis. In particular, their focus is on negation scope detection and negation handling methods. This article also presents some of the challenges and limits of negation accounting in the field of Twitter sentiment analysis.