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2022 | OriginalPaper | Buchkapitel

A Machine Learning Approach for Multiclass Sentiment Analysis of Twitter Data: A Review

verfasst von : Bhagyashree B. Chougule, Ajit S. Patil

Erschienen in: Emerging Technologies in Computer Engineering: Cognitive Computing and Intelligent IoT

Verlag: Springer International Publishing

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Abstract

Sentiment analysis or opinion mining is a prominent and most demanding research topic in today’s world. The main idea behind this research topic is to recognize the user’s opinions and emotions towards the aspect of service or product via a text basis. Sentiment analysis involves mining text, lexicon construction, extracting features and finally finding polarity of text. Even though numerous amounts of research work were conducted in this field through different methods, opinion mining is still considered a challenging field for research.

Most of the prior research concentrated on the binary or ternary classification of sentiments such as positive, negative, neutral. Some studies have done an analysis of Twitter sentiment based on ordinal regression, but by turning the problem of ordinal regression into a problem of binary classification. The aim of this study is to review the multiclass sentiment analysis of Twitter text data using an automated i.e., machine learning approach. This review paper intends to focus on existing work for Twitter sentiment analysis with multiple polarity categorization and explore gaps with future scope in the said research area.

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Vorheriges Kapitel Review on Analysis of Classifiers for Fake News Detection

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Titel: A Machine Learning Approach for Multiclass Sentiment Analysis of Twitter Data: A Review
verfasst von: Bhagyashree B. Chougule
Ajit S. Patil
Verlag: Springer International Publishing
Buch: Emerging Technologies in Computer Engineering: Cognitive Computing and Intelligent IoT
Print ISBN: 978-3-031-07011-2

Electronic ISBN: 978-3-031-07012-9

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-3-031-07012-9_35

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