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2018 | OriginalPaper | Chapter

A Novel Feature-Based Text Classification Improving the Accuracy of Twitter Sentiment Analysis

Authors : Yili Wang, Le Sun, Jin Wang, Yuhui Zheng, Hee Yong Youn

Published in: Advances in Computer Science and Ubiquitous Computing

Publisher: Springer Singapore

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Abstract

With the growth of Internet and various online services, tremendous amount of data are generated in real time. As a result, sentiment analysis of online reviews has become an important research problem. In this paper a novel feature selection and weighting scheme is proposed for the sentiment analysis of twitter data. The Part of Speech (POS) tagging and Bayes-based Classifier are utilized in the proposed scheme. Also, different from the existing schemes, independency of the attributes and the influence of emotional words are properly manipulated in deciding the polarity of test data. Computer simulation with Sentiment 140 workload shows that the proposed scheme significantly outperforms the existing sentiment analysis schemes such as naïve Bayes classifier and selective Bayes classifier.

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Metadata
Title
A Novel Feature-Based Text Classification Improving the Accuracy of Twitter Sentiment Analysis
Authors
Yili Wang
Le Sun
Jin Wang
Yuhui Zheng
Hee Yong Youn
Copyright Year
2018
Publisher
Springer Singapore
Book
Advances in Computer Science and Ubiquitous Computing

Print ISBN: 978-981-10-7604-6

Electronic ISBN: 978-981-10-7605-3

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-981-10-7605-3_72