A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism

Xiang, Zheng; Du, Qianzhou; Ma, Yong; Fan, Weiguo

doi:10.1016/j.tourman.2016.10.001

Cited by 716 publications

(470 citation statements)

References 51 publications

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“…In order to show that data from an OTR appears in search engines (Figure 2), we have chosen an OTR from TripAdvisor, the largest user-generated online review site in the tourism domain [7,39], and the three search engines with the most traffic, Google, Baidu, and Yahoo (Alexa.com, TopSites). It is noteworthy that Yahoo does not use its own means and presents the results obtained by Live.com through Microsoft's Bing search engine.…”

Section: Otrs On Search Enginesmentioning

confidence: 99%

“…According to Xiang et al [7], primary research has traditionally been done by communicationbased studies such as surveys and in-depth interviews, designed to compile data directly from users and consumers. Today, due to the aforementioned potential and exponential growth in the use of social media in travelling, the tourism and hospitality industry appears to be an ideal field for social media analytics.…”

Section: Introductionmentioning

confidence: 99%

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Measuring Destination Image through Travel Reviews in Search Engines

Mariné-Roig

2017

Sustainability

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Abstract:In recent years, mobile phones and access points to free Wi-Fi services have been enhanced, which has made it easier for travellers to share their stories, pictures, and video clips online during a trip. At the same time, online travel review (OTR) websites have grown significantly, allowing users to post their travel experiences, opinions, comments, and ratings in a structured way. Moreover, Internet search engines play a crucial role in locating and presenting OTRs before and throughout a trip. This evolution of social media and information and communication technologies has upset the classic sources of information of the projected tourist destination image (TDI), allowing electronic word-of-mouth to occupy a prominent position. Hence, the aim of this paper is to propose a method based on big data technologies for analysing and measuring the perceived (and transmitted) TDI from OTRs as presented in search engines, emphasising the cognitive, spatial, temporal, evaluative, and affective TDI dimensions. To test this approach, a massive analysis of metadata processed by search engines was performed on 387,414 TripAdvisor OTRs on 'Things to Do' in Île de France, an outstanding smart tourist destination. The results obtained are consistent and allow for the extraction of insights and business intelligence.

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Section: Otrs On Search Enginesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Measuring Destination Image through Travel Reviews in Search Engines

Mariné-Roig

2017

Sustainability

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“…Guo et al [16] identified key topics using LDA, uncovering 19 controllable topics by mining 266,544 online reviews from 25,670 hotels located in 16 countries. Xiang et al [15] measured review data quality using linguistic characteristics; semantic features; and sentiment, rating, and usefulness features. They utilized these features to predict review helpfulness.…”

Section: Content Analysismentioning

confidence: 99%

“…The topic-based sentiment analysis approach was also applied to extract tourism-related topics (e.g., hotel services and prices) using online reviews [14][15][16]. Previous topic-based sentiment analysis studies fall into two categories: topic extraction using topic modeling [14][15][16][17] and polarity classification at the topic level [12,13]. Topic extraction is the process of extracting topics that have been evaluated, while polarity classification at the topic level classifies the sentiments in each topic into positive, negative, or neutral [18].…”

Section: Introductionmentioning

confidence: 99%

Investigating Online Destination Images Using a Topic-Based Sentiment Analysis Approach

Ren

Hong

2017

Sustainability

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With the development of Web 2.0, many studies have tried to analyze tourist behavior utilizing user-generated contents. The primary purpose of this study is to propose a topic-based sentiment analysis approach, including a polarity classification and an emotion classification. We use the Latent Dirichlet Allocation model to extract topics from online travel review data and analyze the sentiments and emotions for each topic with our proposed approach. The top frequent words are extracted for each topic from online reviews on Ctrip.com. By comparing the relative importance of each topic, we conclude that many tourists prefer to provide "suggestion" reviews. In particular, we propose a new approach to classify the emotions of online reviews at the topic level utilizing an emotion lexicon, focusing on specific emotions to analyze customer complaints. The results reveal that attraction "management" obtains most complaints. These findings may provide useful insights for the development of attractions and the measurement of online destination image. Our proposed method can be used to analyze reviews from many online platforms and domains.

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“…However, there is a danger of generalizing results from data that are not representative or have been poorly gathered [22]. Furthermore, as suggested by Hall [23][24][25] and Shoval [26], qualitative and quantitative analyses should be integrated to explore tourist behavior and traditional approaches should not be disregarded.…”

Section: Travel Blog Data and Tourist Behaviormentioning

confidence: 99%

How Can Big Data Support Smart Scenic Area Management? An Analysis of Travel Blogs on Huashan

2017

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Abstract:Data from travel blogs represent important travel behavior and destination resource information. Moreover, technological innovations and increasing use of social media are providing accessible 'big data' at a low cost. Despite this, there is still limited big data analysis for scenic tourism areas. This research on Huashan (Mount Hua, China) data-mined user-contributed travel logs on the Mafengwo and Ctrip websites. Semantic analysis explored tourist movement patterns and preferences within the scenic area. GIS provided a visual distribution of blogger origins. The relationship between Huashan and adjoining tourism areas revealed a multi-destination pattern of tourist movements. Emotional analysis indicated tourist satisfaction levels, while content analysis explored more deeply into dissatisfying aspects of tourist experiences. The results should provide guidance for scenic areas in destination planning and design.

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A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism

Cited by 716 publications

References 51 publications

Measuring Destination Image through Travel Reviews in Search Engines

Measuring Destination Image through Travel Reviews in Search Engines

Investigating Online Destination Images Using a Topic-Based Sentiment Analysis Approach

How Can Big Data Support Smart Scenic Area Management? An Analysis of Travel Blogs on Huashan

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