Extracting and Ranking Travel Tips from User-Generated Reviews

Guy, Ido; Mejer, Avihai; Nus, Alexander; Raiber, Fiana

doi:10.1145/3038912.3052632

Cited by 32 publications

(20 citation statements)

References 38 publications

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“…Quantitative designs (54%) are used more often than other research designs. This approach was mainly applied to studies focused on topics such as use of social media in trip planning (pre, during, and post) (Guy, Mejer, Nus, & Raiber, 2017;Kim, Lee, Shin, & Yang, 2017), consumer adoption of social media (Chung, Han, & Koo, 2015), user-generated content and tourist behaviour (Yu, Carlsson, & Zou, 2014), and influence of factors such as trust and motivation to use social media (Huang et al, 2010;Yoo & Gretzel, 2010). Fewer studies applied qualitative research designs (17%), mostly those focused on topics such as social media content and its characteristics (Ferguson, Piché, & Walby, 2015;Jeacle & Carter, 2011) and understanding of travellers' use of social media (Mendes-Filho, Tan, & Milne, 2010).…”

Section: Research Methodology and Designmentioning

confidence: 99%

Reviewing the Past to Inform the Future: A Literature Review of Social Media in Tourism

Zarezadeh

Rastegar

Gretzel

2018

Czech Journal of Tourism

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Acknowledging the significant advancement of social media, the role and impact of social media has been widely discussed in tourism research. As a result, it has become increasingly difficult to obtain an overview of the knowledge produced in relation to the topic. Review studies provide such overviews to guide future research efforts. This study reviews and analyses 152 tourism-related social media publications since 2008. Based on a qualitative systematic analysis of publications that address the topic from a consumer perspective, the paper identifies publication trends and highlights patterns in the topics, aims, and research designs of existing publications. Specifically, it captures a broader array of consumer-centric topics than previous reviews and highlights methodological gaps. Moreover, the research argues that there is still ample room for more research on social media in tourism, particularly as the existing literature neglects social media beyond review platforms while demonstrating that user-generated content greatly influences tourist behaviours and experiences.

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Section: Research Methodology and Designmentioning

confidence: 99%

Reviewing the Past to Inform the Future: A Literature Review of Social Media in Tourism

Zarezadeh

Rastegar

Gretzel

2018

Czech Journal of Tourism

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“…We formulate the problem of finding useful travel tips and interesting facts as a query-sentence matching problem. We adopt an approach similar to an existing work for mining travel tips from reviews [2]. The approach consists of a filtering phase and a ranking phase.…”

Section: Mining Interesting Facts and Tipsmentioning

confidence: 99%

“…This phase constructs a set of candidate tip/fact sentences by applying filters and classifiers to all the review sentences. According to [2], effective filters for tips include phrase patterns (e.g, sentences containing "make sure to") and part-ofspeech tags (e.g, sentences starting with verbs). For constructing the candidate set of interesting facts, we select sentences that contain a least one informative token, which are words or short phrases frequently mentioned in reviews of the target entity but not frequently mentioned in reviews of similar entities.…”

Section: Mining Interesting Facts and Tipsmentioning

confidence: 99%

Voyageur: An Experiential Travel Search Engine

Evensen¹,

Feng²,

Halevy³

et al. 2019

The World Wide Web Conference

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We describe Voyageur, which is an application of experiential search to the domain of travel. Unlike traditional search engines for online services, experiential search focuses on the experiential aspects of the service under consideration. In particular, Voyageur needs to handle queries for subjective aspects of the service (e.g., quiet hotel, friendly staff) and combine these with objective attributes, such as price and location. Voyageur also highlights interesting facts and tips about the services the user is considering to provide them with further insights into their choices.

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“…Exposing Tourism Indicators as High Quality Linked Data [18] • Using of social network data to know tourist movements [19]. • Knowing tourist perception of destinations [20].…”

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confidence: 99%

BITOUR: A Business Intelligence Platform for Tourism Analysis

Bustamante

Sebastiá

Onaindía

2020

IJGI

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Integrating collaborative data in data-driven Business Intelligence (BI) system brings an opportunity to foster the decision-making process towards improving tourism competitiveness. This article presents BITOUR, a BI platform that integrates four collaborative data sources (Twitter, Openstreetmap, Tripadvisor and Airbnb). BITOUR follows a classical BI architecture and provides functionalities for data transformation, data processing, data analysis and data visualization. At the core of the data processing, BITOUR offers mechanisms to identify tourists in Twitter, assign tweets to attractions and accommodation sites from Tripadvisor and Airbnb, analyze sentiments in opinions issued by tourists, and all this using geolocation objects in Openstreetmap. With all these ingredients, BITOUR enables data analysis and visualization to answer questions like the most frequented places by tourists, the average stay length or the view of visitors of some particular destination.

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Extracting and Ranking Travel Tips from User-Generated Reviews

Cited by 32 publications

References 38 publications

Reviewing the Past to Inform the Future: A Literature Review of Social Media in Tourism

Reviewing the Past to Inform the Future: A Literature Review of Social Media in Tourism

Voyageur: An Experiential Travel Search Engine

BITOUR: A Business Intelligence Platform for Tourism Analysis

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