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Target-Oriented Data Annotation for Emotion and Sentiment Analysis in Tourism Related Social Media Data

Author

Listed:
  • Alireza Alaei

    (Faculty of Science and Engineering, Gold Coast Campus, Southern Cross University, Gold Coast, QLD 4225, Australia)

  • Ying Wang

    (School of Hotel and Tourism Management, Hong Kong Polytechnic University, Hong Kong)

  • Vinh Bui

    (Faculty of Science and Engineering, Gold Coast Campus, Southern Cross University, Gold Coast, QLD 4225, Australia)

  • Bela Stantic

    (School of Information and Communication Technology, Griffith University, Gold Coast, QLD 4222, Australia)

Abstract

Social media have been a valuable data source for studying people’s opinions, intentions, and behaviours. Such a data source incorporating advanced big data analysis methods, such as machine-operated emotion and sentiment analysis, will open unprecedented opportunities for innovative data-driven destination monitoring and management. However, a big challenge any machine-operated text analysis method faces is the ambiguity of the natural languages, which may cause an expression to have different meanings in different contexts. In this work, we address the ambiguity challenge by proposing a context-aware dictionary-based target-oriented emotion and sentiment analysis method that incorporates inputs from both humans and machines to introduce an alternative approach to measuring emotions and sentiment in limited tourism-related data. The study makes a methodological contribution by creating a target dictionary specifically for tourism sentiment analysis. To demonstrate the performance of the proposed method, a case of target-oriented emotion and sentiment analysis of posts from Twitter for the Gold Coast of Australia as a tourist destination was considered. The results suggest that Twitter data cover a broad range of destination attributes and can be a valuable source for comprehensive monitoring of tourist experiences at a destination.

Suggested Citation

  • Alireza Alaei & Ying Wang & Vinh Bui & Bela Stantic, 2023. "Target-Oriented Data Annotation for Emotion and Sentiment Analysis in Tourism Related Social Media Data," Future Internet, MDPI, vol. 15(4), pages 1-21, April.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:4:p:150-:d:1126841
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    References listed on IDEAS

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    1. Marco Rossetti & Fabio Stella & Markus Zanker, 2016. "Analyzing user reviews in tourism with topic models," Information Technology & Tourism, Springer, vol. 16(1), pages 5-21, March.
    2. Shanshi Li & Noel Scott & Gabby Walters, 2015. "Current and potential methods for measuring emotion in tourism experiences: a review," Current Issues in Tourism, Taylor & Francis Journals, vol. 18(9), pages 805-827, September.
    3. Chaochang Chiu & Nan-Hsing Chiu & Re-Jiau Sung & Pei-Yu Hsieh, 2015. "Opinion mining of hotel customer-generated contents in Chinese weblogs," Current Issues in Tourism, Taylor & Francis Journals, vol. 18(5), pages 477-495, May.
    4. Eunil Park & Jiwon Kang & Daejin Choi & Jinyoung Han, 2020. "Understanding customers' hotel revisiting behaviour: a sentiment analysis of online feedback reviews," Current Issues in Tourism, Taylor & Francis Journals, vol. 23(5), pages 605-611, March.
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    Cited by:

    1. Zhongzhong Zeng & Meizhu Wang & Dingyi Liu & Xuan Yu & Bo Zhang, 2024. "A Semantic Analysis Method of Public Public Built Environment and Its Landscape Based on Big Data Technology: Kimbell Art Museum as Example," Land, MDPI, vol. 13(5), pages 1-16, May.

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