Survey on Classic and Latest Textual Sentiment Analysis Articles and Techniques

Yong Shi, Luyao Zhu, Wei Li, Kun Guo and Yuanchun Zheng
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Yong Shi: School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, P. R. China†Research Center on Fictitious Economy & Data Science, Chinese Academy of Sciences, Beijing 100190, P. R. China‡Key Laboratory of Big Data Mining and knowledge Management, Chinese Academy of Sciences, Beijing 100190, P. R. China¶College of Information Science and Technology, University of Nebraska at Omaha, NE 68182, USA
Luyao Zhu: School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, P. R. China†Research Center on Fictitious Economy & Data Science, Chinese Academy of Sciences, Beijing 100190, P. R. China‡Key Laboratory of Big Data Mining and knowledge Management, Chinese Academy of Sciences, Beijing 100190, P. R. China
Wei Li: School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, P. R. China†Research Center on Fictitious Economy & Data Science, Chinese Academy of Sciences, Beijing 100190, P. R. China‡Key Laboratory of Big Data Mining and knowledge Management, Chinese Academy of Sciences, Beijing 100190, P. R. China
Kun Guo: School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, P. R. China†Research Center on Fictitious Economy & Data Science, Chinese Academy of Sciences, Beijing 100190, P. R. China‡Key Laboratory of Big Data Mining and knowledge Management, Chinese Academy of Sciences, Beijing 100190, P. R. China
Yuanchun Zheng: #x2020;Research Center on Fictitious Economy & Data Science, Chinese Academy of Sciences, Beijing 100190, P. R. China‡Key Laboratory of Big Data Mining and knowledge Management, Chinese Academy of Sciences, Beijing 100190, P. R. China§School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing 100190, P. R. China

International Journal of Information Technology & Decision Making (IJITDM), 2019, vol. 18, issue 04, 1243-1287

Abstract: Text is a typical example of unstructured and heterogeneous data in which massive useful knowledge is embedded. Sentiment analysis is used to analyze and predict sentiment polarities of the text. This paper provides a survey and gives comparative analyses of the latest articles and techniques pertaining to lexicon-based, traditional machine learning-based, deep learning-based, and hybrid sentiment analysis approaches. These approaches have their own superiority and get the state-of-the-art results on diverse sentiment analysis tasks. Besides, a brief sentiment analysis example in the tourism domain is displayed, illustrating the entire process of sentiment analysis. Furthermore, we create a large table to compare the pros and cons of different types of approaches, and discuss some insights with respect to research trends. In addition, a lot of important sentiment analysis datasets are summarized in this survey.

Keywords: Sentiment analysis; sentiment lexicon; hybrid approach; deep learning (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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DOI: 10.1142/S0219622019300015

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