Topic Modelling of Tourist Dining Experiences Based on the GLOBE Model

The needs of travellers vary across cultures. When it comes to culinary aspects, there is a strong connection between gastronomy and culture. To optimise service offerings, investigation of the essential aspects of dining experiences in relation to cultural backgrounds is of great importance. In the age of digitalisation, tourists share their dining experiences throughout their multiphasic travel journey via online platforms. By considering nine distinct cultural backgrounds, this research aims to investigate tourist experiences based on TripAdvisor restaurant reviews through topic modelling, using the city of Salzburg as its study context. Depending on one’s cultural circumstances, the findings demonstrate that the most important aspects include staff, food-menu items, value for money, restaurant physical appearance, food authenticity, overall service, menu offers, food quality, atmosphere, and recommendations. This study advances the state-of-the-art knowledge of societal culture as a variable in the target market analysis of restaurant customers. Findings allow restaurant owners, other tourism service providers, and destination management organisations to analyse and adapt their service offerings and strategies accordingly.

RECURRENT LEXIS AND PHRASEOLOGY IN ENGLISH RESTAURANT REVIEWS: A DATA-DRIVEN ANALYSIS

This paper presents a corpus linguistic analysis of recurrent vocabulary and phraseologyin written English food discourse. More specifi cally, it focuses on the use and discourse functions of keywords, key multi-word terms and lexical bundles in a specialized corpus comprising 200 professional restaurant reviews that were published in online editions of selected British and American newspapers. The results of the study indicate that the most distinctive lexical feature of the analyzed texts is the frequent mention of ingredients and the limited presence of stance devices. The most frequently mentioned aspects of the referential content also show that what is evaluated is the total experience of eating and dining at a restaurant. These fi ndings contribute to the area of English forSpecific Purposes, off ering pedagogical potential that can be exploited when developing purpose-made teaching materials for students in food-related programs who need to learn the specialized vocabulary of their target profession.

The Impact of Increase in Minimum Wages on Consumer Perceptions of Service: A Transformer Model of Online Restaurant Reviews

We study the impact of an increase in minimum wages on consumer perceptions of service quality in the restaurant industry.

The effect of online reviews on restaurant visit intentions: applying signaling and involvement theories

Purpose The purpose of this study is to investigate the effect of online reviews on consumers’ intention to visit restaurants, with the moderating role of involvement. Design/methodology/approach The research framework was built on signaling theory, message appeals and involvement theory. To test the proposed framework, three experiments were conducted online with real customer samples. T-tests, ANOVA and SPSS PROCESS macro were used for data analysis. Findings The results revealed that online reviews with higher online ratings and emotional appeal led to higher restaurant visit intention. Review appeal significantly moderated the effect of online ratings on restaurant visit intention. Customers with low restaurant involvement were more impacted by emotional comments than by functional comments. Research limitations/implications The present study extends our knowledge on the effects of online reviews moderated by levels of customer involvement. By combining signaling theory with involvement theory, it adds value to the literature on customer online behavior, especially in the foodservice context. The present study has limitations that might provide opportunities for future research. It used evaluations (TripAdvisor scores) and only positive reviews (texts), so customers’ intentions considering negative reviews could not be examined. The level of hedonism concerning consumption in restaurants and prior knowledge regarding restaurant reviews was not controlled for. It is possible that the level of hedonism perceived and prior review knowledge may moderate the customers’ intention to visit the restaurant. Practical implications The present study shows the importance of online comments for the promotion of restaurants that have low evaluation scores. It is essential that restaurant owners and managers encourage potential customers by using comments to elaborate on their marketing strategies and promotion. At the same time, they should invite customers to share their emotional experiences, and not just their views on service efficiency (a functional aspect). During the COVID-19 pandemic, the use of the internet and mobile devices has become more prominent. Managers could therefore use emotional messages on the restaurant’s website or apps to attract customers with low restaurant involvement. Also, a system to identify the involvement of customers with restaurants could be implemented online or on mobile devices to present specific messages. The present study also recommends the use of online tools as virtual tours, photographs taken from different angles, smiling faces, floor plans and sittings and pre-determined emotional expressions. Also, the restaurant could promote lives on cooking different dishes to motive customer’s interaction and comments. These would help to increase customers’ visit intentions. Originality/value This study extends knowledge about the effect of restaurant online reviews (both ratings and appeals) moderated by the level of customer involvement. The present study also adds value to the customer online behavior literature showing that customers with low involvement are more sensitive to emotional content as they use the affective route to process information rather than the central route.

Improving Sentiment Classification of Restaurant Reviews with Attention-Based Bi-GRU Neural Network

In the era of Web 2.0, there is a huge amount of user-generated content, but the huge amount of unstructured data makes it difficult for merchants to provide personalized services and for users to extract information efficiently, so it is necessary to perform sentiment analysis for restaurant reviews. The significant advantage of Bi-GRU is the guaranteed symmetry of the hidden layer weight update, to take into account the context in online restaurant reviews and to obtain better results with fewer parameters, so we combined Word2vec, Bi-GRU, and Attention method to build a sentiment analysis model for online restaurant reviews. Restaurant reviews from Dianping.com were used to train and validate the model. With F1-score greater than 89%, we can conclude that the comprehensive performance of the Word2vec+Bi-GRU+Attention sentiment analysis model is better than the commonly used sentiment analysis models. We applied deep learning methods to review sentiment analysis in online food ordering platforms to improve the performance of sentiment analysis in the restaurant review domain.