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Sentiment classification in social media data by combining triplet belief functions

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  • Yaxin Bi

Abstract

Sentiment analysis is an emerging technique that caters for semantic orientation and opinion mining. It is increasingly used to analyze online reviews and posts for identifying people's opinions and attitudes to products and events in order to improve business performance of companies and aid to make better organizing strategies of events. This paper presents an innovative approach to combining the outputs of sentiment classifiers under the framework of belief functions. It consists of the formulation of sentiment classifier outputs in the triplet evidence structure and the development of general formulas for combining triplet functions derived from sentiment classification results via three evidential combination rules along with comparative analyses. The empirical studies have been conducted on examining the effectiveness of our method for sentiment classification individually and in combination, and the results demonstrate that the best combined classifiers by our method outperforms the best individual classifiers over five review datasets.

Suggested Citation

  • Yaxin Bi, 2022. "Sentiment classification in social media data by combining triplet belief functions," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(7), pages 968-991, July.
    • Handle: RePEc:bla:jinfst:v:73:y:2022:i:7:p:968-991
    DOI: 10.1002/asi.24605
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    References listed on IDEAS

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    1. Nicola Burns & Yaxin Bi & Hui Wang & Terry Anderson, 2019. "Enhanced Twofold-LDA Model for Aspect Discovery and Sentiment Classification," International Journal of Knowledge-Based Organizations (IJKBO), IGI Global, vol. 9(4), pages 1-20, October.
    2. David Vilares & Miguel A. Alonso & Carlos Gómez-Rodríguez, 2015. "On the usefulness of lexical and syntactic processing in polarity classification of Twitter messages," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(9), pages 1799-1816, September.
    3. Javier Rodríguez‐Vidal & Julio Gonzalo & Laura Plaza & Henry Anaya Sánchez, 2019. "Automatic detection of influencers in social networks: Authority versus domain signals," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 70(7), pages 675-684, July.
    4. Prabowo, Rudy & Thelwall, Mike, 2009. "Sentiment analysis: A combined approach," Journal of Informetrics, Elsevier, vol. 3(2), pages 143-157.
    5. Maayan Zhitomirsky‐Geffet & Judit Bar‐Ilan & Mark Levene, 2018. "Categorical relevance judgment," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 69(9), pages 1084-1094, September.
    6. Kasturi Dewi Varathan & Anastasia Giachanou & Fabio Crestani, 2017. "Comparative opinion mining: A review," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(4), pages 811-829, April.
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    Cited by:

    1. Zhenhua Wang & Simin He & Guang Xu & Ming Ren, 2024. "Will sentiment analysis need subculture? A new data augmentation approach," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 75(6), pages 655-670, June.

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