Opinion mining from online travel reviews: A comparative analysis of Chinese major OTAs using semantic association analysis

Hou, Zhiping; Cui, Fasheng; Meng, Yongheng; Lian, Tonghui; Yu, Chongchong

doi:10.1016/j.tourman.2019.03.009

Cited by 112 publications

(93 citation statements)

References 65 publications

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“…In the three columns, there are keywords related to positive feelings (e.g., good, great). The crowdedness-related keywords crowd/s/ed/ing (7386), overcrowd/ed/ing (186), busy (2623), line/s (3392), queue/s (1139), wait/ed/ing (5243), and await/ing (72), together with ticket/s/ing (8563), and the dirtiness-related keywords abandoned (79), derelict (11), dirty (293), garbage (53), junk (101), rubbish (51), ruined (230), and trash (75), together with graffiti (420), that may indicate sustainability problems, appear in the attractions column. The riskiness-related keywords crime/inal/inally (103), danger/ous (122), pick pocket pickpocket/s (422), robbed robbery (66), steal stole/n (416), and thief/ves (97) also appear in the attractions column.…”

Section: Resultsmentioning

confidence: 99%

“…A study focused on exploring similarities between attractions through 1,695,333 OTRs that highlighted Athens, Cairo and Rome in the category of ancient ruins deserves special attention [100]. As subsequent research has shown, people who use reviews of various tourism products have particular objectives, including opinion mining [101], especially for information about tourist satisfaction [102] and affective image [21]. Recent research based on 25,220 TripAdvisor reviews on things to do in an Italian province [103] is not focused on TDI, but it measures visitor satisfaction through its evaluation that uses between one and five bubbles and implements a content analysis using the commercial application Leximancer.…”

Section: Use Of Big Data Analytics In Hospitality and Tourism Researchmentioning

confidence: 99%

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Destination Image Analytics Through Traveller-Generated Content

Mariné-Roig

2019

Sustainability

119

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The explosion of content generated by users, in parallel with the spectacular growth of social media and the proliferation of mobile devices, is causing a paradigm shift in research. Surveys or interviews are no longer necessary to obtain users’ opinions, because researchers can get this information freely on social media. In the field of tourism, online travel reviews (OTRs) hosted on travel-related websites stand out. The objective of this article is to demonstrate the usefulness of OTRs to analyse the image of a tourist destination. For this, a theoretical and methodological framework is defined, as well as metrics that allow for measuring different aspects (designative, appraisive and prescriptive) of the tourist image. The model is applied to the region of Attica (Greece) through a random sample of 300,000 TripAdvisor OTRs about attractions, activities, restaurants and hotels written in English between 2013 and 2018. The results show trends, preferences, assessments, and opinions from the demand side, which can be useful for destination managers in optimising the distribution of available resources and promoting sustainability.

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Section: Resultsmentioning

confidence: 99%

Section: Use Of Big Data Analytics In Hospitality and Tourism Researchmentioning

confidence: 99%

Destination Image Analytics Through Traveller-Generated Content

Mariné-Roig

2019

Sustainability

119

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“…We fill this gap and contribute to future studies. The conclusions drawn from these methods are more objective and can provide more valuable and applicable suggestions for policymakers [ 9 ]. Also, the web crawler technology and text mining method used in this study might be applied to other similar epidemics or in other countries.…”

Section: Introductionmentioning

confidence: 99%

The influence of COVID-19 on agricultural economy and emergency mitigation measures in China: A text mining analysis

et al. 2020

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Understanding the influence of COVID-19 on China's agricultural economy and the Chinese government's emergency measures to ease the economic impacts of viral spread can offer urgently-needed lessons while this virus continues to spread across the globe. Thus, this study collected over 750,000 words upon the topic of COVID-19 and agriculture from the largest two media channels in China: WeChat and Sina Weibo, and employed web crawler technology and text mining method to explore the influence of COVID-19 on agricultural economy and mitigation measures in China. The results show that: (1) the impact of COVID-19 on China's agricultural economy at the very first phase is mainly reflected in eight aspects as crop production, agricultural products supply, livestock production, farmers' income and employment, economic crop development, agricultural products sales model, leisure agriculture development, and agricultural products trade. (2) The government's immediate countermeasures include resuming agricultural production and farmers' work, providing financial support, stabilizing agricultural production and products supply, promoting agricultural products sale, providing subsidies, providing agricultural technology guidance and field management, and providing assistance to poor farmers to reduce poverty. (3) The order of government's immediate countermeasures is not all in line with the order of impact aspects, which indicates that more-tailored policies should be implemented to mitigate the strikes of COVID-19 on China's agricultural economy in the future.

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“…For tourists, online reviews are not only a way of securing information but also the essence of making tourism decisions (Hou et al, 2019). Tourists or consumers often gather destination information before making a trip to a particular destination.…”

Section: Litrerature Reviewmentioning

confidence: 99%

Reflections of destination quality on destination image: A case of Kuşadasi

Janet¹,

Özdemir²

2020

Turizam

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The image of a tourism destination is assembled on various resources including past encounters and marketing communications. However, the utmost important factor in increasing tourist arrivals is creating a high quality of service at a destination in a sustainable manner. The main purpose of this study is to analyze both what image the tourists have about Kuşadası as exemplified in the 15,047 reviews on TripAdvisor through content analysis, and what image the stakeholders have in their minds through interviews. The importance of this study is that it sheds light on the destination image that is stimulated through tourists' user-generated content based on the destination quality attributes.

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Opinion mining from online travel reviews: A comparative analysis of Chinese major OTAs using semantic association analysis

Cited by 112 publications

References 65 publications

Destination Image Analytics Through Traveller-Generated Content

Destination Image Analytics Through Traveller-Generated Content

The influence of COVID-19 on agricultural economy and emergency mitigation measures in China: A text mining analysis

Reflections of destination quality on destination image: A case of Kuşadasi

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