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Survey on Classic and Latest Textual Sentiment Analysis Articles and Techniques

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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)

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.

Suggested Citation

  • Yong Shi & Luyao Zhu & Wei Li & Kun Guo & Yuanchun Zheng, 2019. "Survey on Classic and Latest Textual Sentiment Analysis Articles and Techniques," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(04), pages 1243-1287, July.
    • Handle: RePEc:wsi:ijitdm:v:18:y:2019:i:04:n:s0219622019300015
    DOI: 10.1142/S0219622019300015
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    References listed on IDEAS

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    1. Sanjiv R. Das & Mike Y. Chen, 2007. "Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web," Management Science, INFORMS, vol. 53(9), pages 1375-1388, September.
    2. Kim, Kun & Park, Oun-joung & Yun, Seunghyun & Yun, Haejung, 2017. "What makes tourists feel negatively about tourism destinations? Application of hybrid text mining methodology to smart destination management," Technological Forecasting and Social Change, Elsevier, vol. 123(C), pages 362-369.
    3. Prabowo, Rudy & Thelwall, Mike, 2009. "Sentiment analysis: A combined approach," Journal of Informetrics, Elsevier, vol. 3(2), pages 143-157.
    4. Subarno Pal & Soumadip Ghosh & Amitava Nag, 2018. "Sentiment Analysis in the Light of LSTM Recurrent Neural Networks," International Journal of Synthetic Emotions (IJSE), IGI Global, vol. 9(1), pages 33-39, January.
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    Cited by:

    1. Manosso, Franciele Cristina & Domareski Ruiz, Thays Cristina, 2021. "Using sentiment analysis in tourism research: A systematic, bibliometric, and integrative review," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 7, pages 16-27.
    2. Cristina Franciele & Thays Christina Domareski Ruiz, 2021. "Using sentiment analysis in tourism research: A systematic, bibliometric, and integrative review," Post-Print hal-03373984, HAL.

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