Social media analytics: Extracting and visualizing Hilton hotel ratings and reviews from TripAdvisor

Yung-Chun Chang, Chih-Hao Ku and Chun-Hung Chen

International Journal of Information Management, 2019, vol. 48, issue C, 263-279

Abstract: Analyzing and extracting insights from user-generated data has become a topic of interest among businesses and research groups because such data contains valuable information, e.g., consumers’ opinions, ratings, and recommendations of products and services. However, the true value of social media data is rarely discovered due to overloaded information. Existing literature in analyzing online hotel reviews mainly focuses on a single data resource, lexicon, and analysis method and rarely provides marketing insights and decision-making information to improve business’ service and quality of products. We propose an integrated framework which includes a data crawler, data preprocessing, sentiment-sensitive tree construction, convolution tree kernel classification, aspect extraction and category detection, and visual analytics to gain insights into hotel ratings and reviews. The empirical findings show that our proposed approach outperforms baseline algorithms as well as well-known sentiment classification methods, and achieves high precision (0.95) and recall (0.96). The visual analytics results reveal that Business travelers tend to give lower ratings, while Couples tend to give higher ratings. In general, users tend to rate lowest in July and highest in December. The Business travelers more frequently use negative keywords, such as “rude,” “terrible,” “horrible,” “broken,” and “dirty,” to express their dissatisfied emotions toward their hotel stays in July.

Keywords: Sentiment analysis; Hospitality; Natural language processing; Social media analytics; Visual analytics; Google trends; TripAdvisor (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ininma:v:48:y:2019:i:c:p:263-279

DOI: 10.1016/j.ijinfomgt.2017.11.001

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