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

2018 ◽  
Vol 71 ◽  
pp. 19-32 ◽  
Author(s):  
Song Gao ◽  
Ou Tang ◽  
Hongwei Wang ◽  
Pei Yin
Keyword(s):  
Online Reviews ◽  
Restaurant Industry ◽  
Relation Mining

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

2019 ◽  
Vol 11 (19) ◽  
pp. 5254 ◽  
Author(s):  
Yi Luo ◽  
Xiaowei Xu
Keyword(s):  
Latent Dirichlet Allocation ◽  
Online Reviews ◽  
Restaurant Industry ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Plain Text ◽  
Sustainable Marketing ◽  
Restaurant Reviews ◽  
Negative Sentiment ◽  
Sustainable Competitive Advantages

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.

Competitiveness analysis through comparative relation mining

2017 ◽  
Vol 117 (4) ◽  
pp. 672-687 ◽  
Author(s):  
Hongwei Wang ◽  
Song Gao ◽  
Pei Yin ◽  
James Nga-Kwok Liu
Keyword(s):  
Online Reviews ◽  
Purchase Decisions ◽  
Content Type ◽  
Online Consumer Reviews ◽  
Product Features ◽  
Proposed Model ◽  
Set Up ◽  
Comparative Relations ◽  
Sequence Rule ◽  
Relation Mining

Purpose Comparative opinions widely exist in online reviews as a common way of expressing consumers’ ideas or preferences toward certain products. Such opinion-rich texts are key proxies for detecting product competitiveness. The purpose of this paper is to set up a model for competitiveness analysis by identifying comparative relations from online reviews for restaurants based on both pattern matching and machine learning. Design/methodology/approach The authors define the sub-category of comparative sentences according to Chinese linguistics. Classification rules are set up for each type of comparative relations through class sequence rule. To improve the accuracy of classification, a comparative entity dictionary is then introduced for further identifying comparative sentences. Finally, the authors collect reviews for restaurants from Dianping.com to conduct experiments for testing the proposed model. Findings The experiments show that the proposed method outperforms the baseline methods in terms of precision in identifying comparative sentences. On the basis of such comparison-rich sentences, product features and comparative relations are extracted for sentiment analysis, and sentimental score is assigned to each comparative relation to facilitate competitiveness analysis. Research limitations/implications Only the explicit comparative relations are discussed, neglecting the implicit ones. Besides that, the study is grounded in the assumption that all features are homogeneous. In some cases, however, the weights to different aspects are not of the same importance to market. Practical implications On the basis of comparative relation mining, product features and comparative opinions are extracted for competitiveness analysis, which is of interest to businesses for finding weakness or strength of products, as well as to consumers for making better purchase decisions. Social implications Comparative relation mining could be possibly applied in social media for identifying relations among users or products, and ranking users or products, as well as helping companies target and track competitors to enhance competitiveness. Originality/value The authors propose a research framework for restaurant competitiveness analysis by mining comparative relations from online consumer reviews. The results would be able to differentiate one restaurant from another in some aspects of interest to consumers, and reveal the changes in these differences over time.

Comparative Relation Mining of Online Reviews: A Hierarchical Multi-attention Network Model

2023 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Jiaqi Liu ◽  
Hongwei Wang ◽  
Song Gao ◽  
Yuanjun Zhu ◽  
Ou Tang
Keyword(s):  
Network Model ◽  
Online Reviews ◽  
Attention Network ◽  
Relation Mining

Seeking reward or avoiding risk from restaurant reviews: does distance matter?

2019 ◽  
Vol 31 (12) ◽  
pp. 4482-4499 ◽  
Author(s):  
Esther L. Kim ◽  
Sarah Tanford
Keyword(s):  
Critical Role ◽  
Online Reviews ◽  
Restaurant Industry ◽  
Risk Avoidance ◽  
Content Type ◽  
Customer Base ◽  
Restaurant Reviews ◽  
Gains And Losses ◽  
Practical Implications

Purpose The purpose of this paper is to evaluate the extent to which consumers will exert more effort to avoid risk (negative reviews) versus seek reward (positive reviews) when making a restaurant decision. Design/methodology/approach This study investigates the influence of distance and review valence on restaurant decisions. A 2 (base restaurant review valence: negative, neutral) × 2 (target restaurant review valence: neutral, positive) × 2 (distance: 30 min, 60 min) between-subjects factorial design was used. Findings People exert more effort to seek a reward versus avoid a risk. People will drive any distance to dine at a restaurant with positive reviews. However, the tendency to avoid a restaurant with negative reviews declines as distance increases. Practical implications This study emphasizes the critical role of positive reviews in the restaurant industry. This research provides guidance to operators to manage online reviews effectively. The marketing strategy taking into account review valence and distance allows the business to attract new customers and grow its customer base. Originality/value This research synthesizes asymmetry effects and prospect theory with the level of risk associated with the outcome. This research is theoretically noteworthy since the finding of a reverse asymmetry principle is in contrast with the traditional belief of risk-avoidance when comparing gains and losses.

scholarly journals Insights from sentiment analysis to leverage local tourism business in restaurants

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ting Yu ◽  
Paulo Rita ◽  
Sérgio Moro ◽  
Cristina Oliveira
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Online Reviews ◽  
Restaurant Industry ◽  
Bench Mark ◽  
Quality Factors ◽  
Vast Number ◽  
Content Type ◽  
Food And Beverage

Purpose Social media has become the main venue for users to express their opinions and feelings, generating a vast number of available and valuable data to be scrutinized by researchers and marketers. This paper aims to extend previous studies analyzing social media reviews through text mining and sentiment analysis to provide useful recommendations for management in the restaurant industry. Design/methodology/approach The Lexalytics, a text mining artificial intelligence tool, is applied to analyze the text of the online reviews of the restaurants in a touristic Dutch village extracted from the most frequently used social media platforms focusing on the four restaurant quality factors, namely, food and beverage, service, atmosphere and value. Findings The findings of this research are presented by the identified key themes with comparisons of the customers’ review sentiment between a selected restaurant, Zwaantje, vis-à-vis its bench-mark restaurants set by a specific approach under the abovementioned quality dimensions, in which the food and beverage and service are the most commented by customers. Results demonstrate that text mining can generate insights from different aspects and that the proposed approach is valuable to restaurant management. Originality/value The paper provides a relatively big scale in numbers and resources of social media reviews to further explore the most important service dimensions in the restaurant industry in a specific tourist area. It also offers a useful framework to apply the text mining business intelligence tool by comparison of peers for local small business restaurant practitioners to improve their management skills beyond manually reading social media reviews.

scholarly journals Exploring Consumer Emotions in Pre-Pandemic and Pandemic Times. A Sentiment Analysis of Perceptions in the Fine-Dining Restaurant Industry in Bucharest, Romania

2021 ◽  
Vol 18 (24) ◽  
pp. 13300
Author(s):  
Jacqueline-Nathalie Harba ◽  
Gabriela Tigu ◽  
Adriana AnaMaria Davidescu
Keyword(s):  
Content Analysis ◽  
Sentiment Analysis ◽  
Online Reviews ◽  
Restaurant Industry ◽  
Capital City ◽  
Choice Making ◽  
Consumer Emotions ◽  
Cognitive Choice ◽  
Media Sentiment

This research paper aims to analyse how consumer emotions have evolved during the pandemic period in comparison with the pre-pandemic period in relation to restaurant demand in the Romanian fine-dining industry and uses valuable information based on social-media sentiment analysis and content analysis. Focusing on theories of consumer behaviour, the study aims to emphasize how, under the influence of an epidemic crisis caused by an infectious disease, individual behaviour adapts to the “new normal”, embracing a series of changes in the preferences, attitudes, and cognitive choice-making processes. The article takes into account a comparative analysis of the consumer emotions between the pre-COVID-19 pandemic period (2010–2019) and the pandemic period (2020–present), based on the online reviews provided by customers for five fine-dining restaurants from Bucharest, the capital city of Romania: The Artist, Relais & Chateaux Le Bistrot Francais, Casa di David, Kaiamo, and L’Atelier. The research was based on two mining analyses—content analysis and sentiment analysis—and explored the emotional intent of words, with the data being collected from TripAdvisor through web-scrapping. The empirical results defined the fine-dining experience during the pandemic as being associated with the quality of the dishes and also with the quality of the service. The overall consumer sentiment in the direction of the restaurants analyzed is positive. The sentiment research found that throughout the epidemic, the consumers’ attitudes about restaurants deteriorated. In this sense, consumers seem to be less satisfied with the restaurants’ services than before the pandemic. This is another thing that the restaurants had difficulties in when adapting their operations for the pandemic.

Stressor effects of negative online reviews on anger and burnout in the restaurant industry

2017 ◽  
Vol 29 (11) ◽  
pp. 2847-2866 ◽  
Author(s):  
Karin Weber ◽  
Graham L. Bradley ◽  
Beverley Sparks
Keyword(s):  
Structural Equation ◽  
Hospitality Industry ◽  
Online Reviews ◽  
Restaurant Industry ◽  
Equation Modeling ◽  
Social Stressors ◽  
Content Type ◽  
Face To Face ◽  
Personal Impact ◽  
The Impact

Purpose Owners, managers and employees may be criticized personally and professionally by consumers in online reviews, and may suffer emotional and burnout consequences. The purpose of this paper is to examine the impact of customer-generated negative online reviews on hospitality employees. Design/methodology/approach This research analyzed the effects of traditional face-to-face customer-related social stressors, as well as a newly added negative online review (NOR) stressor, on anger and burnout in a sample of 418 US hospitality workers. Findings Structural equation modeling revealed that, after taking into account the contribution of customer-related social stressors, receipt of NORs predicts anger and anger mediates the relationships between NOR-receipt and two indices of burnout. Practical implications This research extends our understanding of social stressors that apply to workers in the hospitality industry. It offers strategies for managing the threats and optimizing the opportunities, provided by negative online reviews. Originality/value This study is one of the first studies that provide evidence of the personal impact of NORs on hospitality industry employees, thereby extending our understanding of social stressors that apply to workers in this industry.

scholarly journals The Impact of Social Media on Restaurant Corporations’ Financial Performance

2020 ◽  
Vol 12 (4) ◽  
pp. 1646 ◽  
Author(s):  
Sergio M. Fernández-Miguélez ◽  
Miguel Díaz-Puche ◽  
Juan A. Campos-Soria ◽  
Federico Galán-Valdivieso
Keyword(s):  
Social Media ◽  
Financial Performance ◽  
Total Population ◽  
Essential Element ◽  
Online Reviews ◽  
Restaurant Industry ◽  
Nonparametric Techniques ◽  
The Impact ◽  
Positive Effect ◽  
Corporate Level

Social media, in the form of online reviews (ORs), has become an essential element for consumers in the restaurant industry, providing reliable and unbiased information based on the dining experiences of other consumers. Social media is not only a crucial phenomenon for the strategy of restaurants, but also for their corporations. However, previous literature has focused on the analysis at the establishment level, rather than at the corporate level, especially when referring to financial performance. The present study tries to verify if social media also affects corporate financial performance. For this, the impact of ORs on advanced measures of financial performance was examined at the corporate level on a sample of 800 restaurants selected from the total population of active restaurants in Europe in 2018. The investigation applied both regression analysis and nonparametric techniques. They demonstrate a positive effect of ORs on financial performance, and a heterogeneous relationship between both variables across the European countries. Restaurants are becoming aware of the implications of this phenomenon since it could provide strategies for sustainable economic development.

The stress of anonymous online reviews: a conceptual model and research agenda

2015 ◽  
Vol 27 (5) ◽  
pp. 739-755 ◽  
Author(s):  
Graham L. Bradley ◽  
Beverley A. Sparks ◽  
Karin Weber
Keyword(s):  
Research Agenda ◽  
Online Reviews ◽  
Social Consequences ◽  
Restaurant Industry ◽  
Professional Relationships ◽  
Stress Reactions ◽  
Loss Of Face ◽  
Content Type ◽  
Qualitative Survey ◽  
The Impact

Purpose – The paper aims to examine the impact of customer-generated negative online reviews on hospitality employees and businesses. It introduces the concept of negative online review stress, or NOR_Stress (occupational stress due to being targeted by negative online reviews), and present strategies for researching and managing the impact of negative online reviews. Design/methodology/approach – This conceptual paper sets forth a framework, based on the stress, services and hospitality literature, within which negative online reviews, NOR_Stress, and their impact on individuals and businesses can be understood. Aspects of the framework are illustrated by application of online archival material. Findings – The paper demonstrates how negative online reviews can have adverse and diverse effects on restaurant industry employees and businesses. Research limitations/implications – The paper sets out a research agenda relating to negative online reviews and NOR_Stress causes, consequences and countermeasures. Multiple research questions are posed, to be investigated through a combination of qualitative, survey and experimental methods. Practical implications – Four types of countermeasures are presented: preventative, protective, positive and palliative. Social implications – Negative online reviews can exact a hefty toll, potentially resulting not only in reduced customer patronage and company profitability but also in human and social consequences in the form of adverse stress reactions, loss of face and damaged personal and professional relationships. Originality/value – Negative online reviews have proliferated over the past decade and will continue to grow. This paper is the first to critically examine the human and business impacts of this growing threat to the hospitality industry.

scholarly journals Effect of Online Reviews and Crowd Cues on Restaurant Choice of Customer: Moderating Role of Gender and Perceived Crowding

2021 ◽  
Vol 12 ◽  
Author(s):  
Muhammad Asghar Ali ◽  
Ding Hooi Ting ◽  
Muhammad Ahmad-ur-Rahman ◽  
Shoukat Ali ◽  
Falik Shear ◽  
...  
Keyword(s):  
Sampling Technique ◽  
Online Reviews ◽  
Purchase Intentions ◽  
Restaurant Industry ◽  
Survey Method ◽  
Online Review ◽  
Endogenous Variables ◽  
Relationship Of ◽  
The Relationship

This study is aimed to identify the relative (direct) effect of online review ratings and perceived crowding on purchase intentions of a consumer. Our study also investigated the contingent effect of gender and perceived crowding between the relationship of exogenous and endogenous variables. This study was conducted in the Malaysian restaurant industry. We applied the purposive sampling technique to identify respondents, the mall intercept survey method was used for data collection. Smart PLS software was applied for data analysis (200 respondents). This study demonstrates through its results that online review ratings and perceived crowding have a positive effect on purchase intentions of a consumer. Moreover, if a consumer perceives crowding at a restaurant, this has a positive contingent effect on the relationship between review ratings and purchase intentions. This demonstrates that the consumer is more inclined to choose a restaurant with a high online review rating and has high perceived crowding at some unfamiliar place. Lastly, no evidence is found for the gender difference between review rating and purchase intentions; however, gender shows contingent effect and results confirmed that males preferred more crowded restaurants as compared to females. There are theoretical and practical implications for managers in the findings of this study.

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