Insights from sentiment analysis to leverage local tourism business in restaurants

Ting Yu,Cristina Oliveira,Paulo Rita,Sérgio Moro

doi:10.1108/ijcthr-02-2021-0037

Abstract

PurposeSocial 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/approachThe 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.FindingsThe 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/valueThe 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.

Highlights

Social media collects 7.6 billion people in the world and 53 percent of them are active social media users (Shaw, 2018) who have been cultivated and encouraged to share their purchase experiences through online reviews, which have been taken as references for product and service purchases
Social media has become a 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 extends previous studies analyzing social media reviews through text mining and sentiment analysis in order to provide useful recommendations for management in the restaurant industry

Summary

Introduction

Social media collects 7.6 billion people in the world and 53 percent of them are active social media users (Shaw, 2018) who have been cultivated and encouraged to share their purchase experiences through online reviews, which have been taken as references for product and service purchases. To better understand UGC and transfer the available online text reviews into valuable information, text mining and sentiment analysis through business intelligence tools have been developed rapidly in the last few years (Moro et al, 2019). Most of that attention is turned to large restaurants, chains, or online reservations (e.g., Li et al, 2020). Restaurant Zwaantje was chosen to compare with the benchmark restaurants in the area, to explore how a family running restaurant could take advantage from the available social media reviews and text mining tools generating sentiment analysis to support managerial decisions

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Discussion

Conclusion