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Text Analytics of TripAdvisor Reviews on Tourism Destinations

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dc.contributor.advisor Esichaikul, Dr. Vatcharaporn
dc.contributor.author Dahal, Shikshya
dc.contributor.other Dailey, Prof. Matthew N.
dc.contributor.other Witayangkurn, Dr. Apichon
dc.date.accessioned 2020-06-15T00:38:02Z
dc.date.available 2020-06-15T00:38:02Z
dc.date.issued 2020-07
dc.identifier.other AIT
dc.identifier.uri http://www.cs.ait.ac.th/xmlui/handle/123456789/974
dc.description 128 p. en_US
dc.description.abstract Information sharing on online travel sites in the form of online reviews has emerged as the soft component in service-dominant destination management. Thus, text analytics of online reviews can make inferences based on thousands of tourists’ opinions, feedback, and discussion on online travel sites to make the destination management process smart and intelligent. Hence, the main objective of this research study is to extract the underlying topics and sentiment polarity expressed in the online reviews. This research study was carried out scraping 44,161 English reviews of attraction of Nepal including Sights and Landmarks, Nature and Parks, and Museum from TripAdvisor. The topic modeling algorithm (Latent Dirichlet Allocation) identified six topics for Sights and Landmarks Attraction (Ambience, Cost, Route and Transportation, Cultural Traditions, Infrastructure Conservation, and Shopping Market), four topics for Nature and Parks Attraction (Sightseeing, Travel Activities, Ambience, and Cost) and lastly four topics for Museum Attraction (Exhibition, Building/Infrastructure, Ticketing and Facilities, and Mountaineering Information and Collections). Lexicon-based sentiment analysis using VADER was performed to categorize reviews into positive, negative, and neutral classes. The sentiment expressed in topics under all categories were mostly positive. Furthermore, the negative reviews under each topic identified were analyzed to understand the existing problems that tourists mostly complain about. This study finds that the sentiment score calculated by VADER has a moderate positive relationship with the bubble rating provided by the TripAdvisor users. The result suggests that there is a disparity between neutral and negative sentiment scores assigned by TripAdvisor users and VADER. By analyzing the reviews, it was observed that TripAdvisor users tend to give a higher score to negative reviews. en_US
dc.description.sponsorship His Majesty the King's Scholarships en_US
dc.language.iso en en_US
dc.subject Text Analytics, Topic modeling, Sentiment Analysis, LDA Mallet, VADER en_US
dc.title Text Analytics of TripAdvisor Reviews on Tourism Destinations en_US
dc.type Research report en_US

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