Identifying competitors through comparative relation mining of online reviews in the restaurant industry

Gao, Song; Tang, Ou; Wang, Hongwei; Yin, Ping

doi:10.1016/j.ijhm.2017.09.004

Cited by 108 publications

(66 citation statements)

References 37 publications

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“…Among four extracted fundamental dining aspects, taste/food, value, and experience is consistent with prior studies that apply text-mining analysis to discover hidden restaurant aspects in online reviews [73][74][75][76]. However, location is barely mentioned by previous studies.…”

Section: Summary Of Results and Discussionsupporting

confidence: 84%

Predicting the Helpfulness of Online Restaurant Reviews Using Different Machine Learning Algorithms: A Case Study of Yelp

Luo

2019

Sustainability

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Helpful online reviews could be utilized to create sustainable marketing strategies in the restaurant industry, which contributes to national sustainable economic development. This study, the main aspects (including food/taste, experience, location, and value) from 294,034 reviews on Yelp.com were extracted empirically using the Latent Dirichlet Allocation (LDA) and positive and negative sentiment were assigned to each extracted aspect. Positive sentiments were associated with food/taste, while negative sentiments were associated with value. This study further proves a robust classification algorithm based on Support Vector Machine (SVM) with a Fuzzy Domain Ontology (FDO) algorithm outperforms other traditional classification algorithms such as Naïve Bayes (MB) and SVM ontology in predicting the helpfulness of online reviews. This study enriches the literature on managerial aspects of sustainability by analyzing a large amount of plain text data that customers generated. The results of this study could be used as sustainable marketing strategy for review website developers to design sophisticated, intelligence review systems by enabling customers to sort and filter helpful reviews based on their preferences. The extracted aspects and their assigned sentiment could also help restaurateurs better understand how to meet diverse customers’ needs and maintain sustainable competitive advantages.

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Section: Summary Of Results and Discussionsupporting

confidence: 84%

Predicting the Helpfulness of Online Restaurant Reviews Using Different Machine Learning Algorithms: A Case Study of Yelp

Luo

2019

Sustainability

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“…The online review sites and personal blogs provide opinion-rich information that may be explored through textual and sentiment analysis (Gao et al, 2018;Xiang et al, 2017;Cambria, 2016). Social media analytics are increasingly capturing fast-breaking trends on customer sentiments about products, brands and companies.…”

Section: Capturing Datamentioning

confidence: 99%

The use of data-driven technologies for customer-centric marketing

Camilleri¹

2020

IJBDM

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The latest technologies are shifting how businesses capture, analyse and distribute data from the individual users' online activity. Therefore, this contribution critically reviews the latest developments on big data analytics and programmatic advertising. Moreover, it sheds light on the use of blockchain; as this distributed ledger technology provides secure, verified transactions among marketplace stakeholders. The findings suggest that the service providers are increasingly utilising data-driven technologies including programmatic advertising tools to target and re-target individuals online or on their mobile. However, individuals and organisations are becoming increasingly aware on data protection issues, as they often block marketers from tracking them and serving them ads. In conclusion, this contribution puts forward a theoretical framework that explains how, why, where and when practitioners are capturing, analysing and distributing data. In sum, it implies that the data-driven technologies are facilitating the businesses' customer-centric marketing.

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“…The online review sites and personal blogs may provide opinion-rich information that may be explored through textual and sentiment analysis (Cambria, 2016;Gao et al, 2018;Xiang et al, 2017). Social media analytics are increasingly capturing fastbreaking trends on customer sentiments about products, brands and companies.…”

Section: Capturing Datamentioning

confidence: 99%

The use of data-driven technologies in tourism marketing

Camilleri¹

2019

Entrepreneurship, Innovation and Inequality

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Identifying competitors through comparative relation mining of online reviews in the restaurant industry

Cited by 108 publications

References 37 publications

Predicting the Helpfulness of Online Restaurant Reviews Using Different Machine Learning Algorithms: A Case Study of Yelp

Predicting the Helpfulness of Online Restaurant Reviews Using Different Machine Learning Algorithms: A Case Study of Yelp

The use of data-driven technologies for customer-centric marketing

The use of data-driven technologies in tourism marketing

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