A behavioral analysis of web sharers and browsers in Hong Kong using targeted association rule mining

Rong, Jia; Vu, Huy Quan; Law, Rob; Li, Gang

doi:10.1016/j.tourman.2011.08.006

Cited by 84 publications

(51 citation statements)

References 37 publications

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“…In comparison with the other techniques, implementations of association rule learning in tourism research are rather scarce. Documented applications found in literature include tourism product development (Al-Salim, 2008;Liao, Chen, & Deng, 2010), domestic tourist profiling (Emel, Taskin, & Akat, 2007), sharers and browsers of touristic websites (Rong, Vu, Law, & Li, 2012), and change and trend identification in Hong Kong outbound tourism (Law, Rong, Vu, Li, & Lee, 2011). This paper aims to be a methodological contribution to the field of spatiotemporal tourism behaviour research by demonstrating the potential of ad-hoc sensing networks in the non-participatory measurement of small-scale movements.…”

Section: Introductionmentioning

confidence: 99%

Pattern mining in tourist attraction visits through association rule learning on Bluetooth tracking data: A case study of Ghent, Belgium

et al. 2014

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Section: Introductionmentioning

confidence: 99%

Pattern mining in tourist attraction visits through association rule learning on Bluetooth tracking data: A case study of Ghent, Belgium

et al. 2014

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“…To find the important features and gain insights into their interaction in enhancing the eWOM for MGC activities on Twitter, we respectively applied logistic regression analysis [19] and the Association rule [20][21][22] to analyze the tweets gathered from four banks' official Twitter accounts. The first method is powerful in identifying all key features which highly correlate with the eWOM activities.…”

Section: Methods and Experimental Designmentioning

confidence: 99%

Features of Marketer-Generated Content Tweets For Electronic Word of Mouth in Banking Context

Weerawatnodom

Watanapa

2018

KSS

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This study aims to identify the features of Market-Generated Content (MGC) tweets that are posted with a purpose of initiating electronic word of mouth (known as eWOM). A successful tweet is a tweet that earns active participation from the customers, e.g. getting Retweeted (RT) or Favorited (FAV). The phenomenon of eWOM effectively helps to propagate marketing agendas in both short-term marketing campaigns and long-term brand awareness. In the analysis process, logistic regression and Association rule methods were applied to mine the significant features on the MGC posts which were collected from four selected banks in Thailand during a specific period of time. For results, logistic regression indicated a set of features that causes a substantial number of RT and FAV. Additionally, the Apriori algorithm of the Association rule further specified two key features for effective RT and FAV, and it also suggested how to combine other features with those two key features to enhance the gain of RT and FAV.

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“…Most of the restaurant reviews were posted since 2010, and the number of reviews has been increasing in recent years. The fast growing number of restaurant reviews is probably due to the availability of review websites and the change in tourist behavior in information search and sharing about travel (Rong et al 2012). Given that the data set is relatively new, we consider the entire data set in this case study.…”

Section: Data Collectionmentioning

confidence: 99%

Exploring Tourist Dining Preferences Based on Restaurant Reviews

Law

et al. 2017

Journal of Travel Research

Self Cite

111

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Dining is an essential tourism component that attracts significant expenditure from tourists. Tourism practitioners need insights into the dining behaviors of tourists to support their strategic planning and decision making. Traditional surveys and questionnaires are time consuming and inefficient in capturing the complex dining behaviors of tourists at a large scale. Thus far, the understanding about the dining preferences and opinions of different tourist groups is limited. This paper aims to fill the void by presenting a method that utilizes online restaurant reviews and text processing techniques in analyzing the dining behaviors of tourists. The effectiveness of the proposed method is demonstrated in a case study on international tourists visiting Australia using a large-scale data set of more than 40,000 restaurant reviews made by tourists on 2,265 restaurants. The proposed method can help researchers gain comprehensive insights into the dining preferences of tourists.

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A behavioral analysis of web sharers and browsers in Hong Kong using targeted association rule mining

Cited by 84 publications

References 37 publications

Pattern mining in tourist attraction visits through association rule learning on Bluetooth tracking data: A case study of Ghent, Belgium

Pattern mining in tourist attraction visits through association rule learning on Bluetooth tracking data: A case study of Ghent, Belgium

Features of Marketer-Generated Content Tweets For Electronic Word of Mouth in Banking Context

Exploring Tourist Dining Preferences Based on Restaurant Reviews

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