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Social Media Data-Based Sentiment Analysis of Tourists’ Air Quality Perceptions

Author

Listed:
  • Yuguo Tao

    (School of History Culture and Tourism, Jiangsu Normal University, Xuzhou 221116, China)

  • Feng Zhang

    (School of History Culture and Tourism, Jiangsu Normal University, Xuzhou 221116, China)

  • Chunyun Shi

    (School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China)

  • Yun Chen

    (School of History Culture and Tourism, Jiangsu Normal University, Xuzhou 221116, China)

Abstract

Analyzing tourists’ perceptions of air quality is of great significance to the study of tourist experience satisfaction and the image construction of tourism destinations. In this study, using the web crawler technique, we collected 27,500 comments regarding the air quality of 195 of China’s Class 5A tourist destinations posted by tourists on Sina Weibo from January 2011 to December 2017; these comments were then subjected to a content analysis using the Gooseeker, ROST CM (Content Mining System) and BosonNLP (Natural Language Processing) tools. Based on an analysis of the proportions of sentences with different emotional polarities with ROST EA (Emotion Analysis), we measured the sentiment value of texts using the artificial neural network (ANN) machine learning method implemented through a Chinese social media data-oriented Boson platform based on the Python programming language. The content analysis results indicated that in the adaption stage in Sina Weibo, tourists’ perceptions of air quality were mainly positive and had poor air pollution crisis awareness. Objective emotion words exhibited a similarly high proportion as subjective emotion words, indicating that taking both objective and subjective emotion words into account simultaneously helps to comprehensively understand the emotional content of the comments. The sentiment analysis results showed that for the entire text, sentences with positive emotions accounted for 85.53% of the total comments, with a sentiment value of 0.786, which belonged to the positive medium level; the direction of the temporal “up-down-up” changes and the spatial pattern of high in the south and low in the north (while having little difference between the east and the west) were basically consistent with reality. A further exploration of the theoretical basis of the semi-supervised ANN approach or the introduction of other machine learning methods using different data sources will help to analyze this phenomenon in greater depth. The paper provides evidence for new data and methods for air quality research in tourist destinations and provides a new tool for air quality monitoring.

Suggested Citation

  • Yuguo Tao & Feng Zhang & Chunyun Shi & Yun Chen, 2019. "Social Media Data-Based Sentiment Analysis of Tourists’ Air Quality Perceptions," Sustainability, MDPI, vol. 11(18), pages 1-23, September.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:18:p:5070-:d:267840
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    References listed on IDEAS

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    Cited by:

    1. Xin Zhang & Jiaming Liu & He Zhu & Zongcai Huang & Shuying Zhang & Ping Li, 2021. "A Comparative Study of Customer Perceptions of Urban and Rural Bed and Breakfasts in Beijing: An Analysis of Online Reviews," Sustainability, MDPI, vol. 13(20), pages 1-15, October.
    2. Tongtong Jiang & Xiuguo Wu & Yunxiao Yin, 2023. "Logistics Efficiency Evaluation and Empirical Research under the New Retailing Model: The Way toward Sustainable Development," Sustainability, MDPI, vol. 15(20), pages 1-22, October.
    3. 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.
    4. Meijie Chu & Wentao Song & Zeyu Zhao & Tianmu Chen & Yi-chen Chiang, 2024. "Emotional contagion on social media and the simulation of intervention strategies after a disaster event: a modeling study," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-15, December.
    5. Cristina Franciele & Thays Christina Domareski Ruiz, 2021. "Using sentiment analysis in tourism research: A systematic, bibliometric, and integrative review," Post-Print hal-03373984, HAL.
    6. Haiyue Lu & Xiaoping Rui & Gadisa Fayera Gemechu & Runkui Li, 2022. "Quantitative Evaluation of Psychological Tolerance under the Haze: A Case Study of Typical Provinces and Cities in China with Severe Haze," IJERPH, MDPI, vol. 19(11), pages 1-23, May.
    7. Fernando Borrajo-Millán & María-del-Mar Alonso-Almeida & María Escat-Cortes & Liu Yi, 2021. "Sentiment Analysis to Measure Quality and Build Sustainability in Tourism Destinations," Sustainability, MDPI, vol. 13(11), pages 1-14, May.
    8. Xinming Du, 2023. "Symptom or Culprit? Social Media, Air Pollution, and Violence," CESifo Working Paper Series 10296, CESifo.
    9. Zhang, Xiaowei & Yang, Yang & Zhang, Yi & Zhang, Zili, 2020. "Designing tourist experiences amidst air pollution: A spatial analytical approach using social media," Annals of Tourism Research, Elsevier, vol. 84(C).

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