Abstract
Considering the fast growth and spread of the internet and information technologies, gastronomy had to improve due to the excellent communication facility between customer-business and costumer-costumer, showing the incredible power of eWoM (electronic word-of-mouth). The present research intends to do sentiment analysis on the restaurant's reviews to study how they can stay competitive in the market. The proposed methodology adopts a machine learning approach with social media commentaries classified as positive or negative sentiments and then analysed if they relate to the review ratings. The results reveal that although customers from two countries evaluate a restaurant by a similar rating on TripAdvisor, it doesn´t mean that the associated sentiment has the same value as the rating attributed. Sentiment analysis helps understand the motivations of each gastronomic consumer and realise what leads them to create reviews to express their emotions associated with the service.
Top1. Introduction
The rise of the Internet and information technologies has changed the conduct of the tourism industry, especially in gastronomy, enabling individuals to “share their evaluations of products online” (Zhang et al., 2010). Hence, Internet use has become an inter-directional and instant phenomenon among users and companies to communicate, being only conceivable through Web 2.0.
First introduced in 2004, Web 2.0 was defined by Kaplan and Haenlein (2010) as a “platform whereby content and applications [e.g. blogs, web pages or wikis] are no longer created and published by individuals, but instead are continuously modified by all users in a participatory and collaborative fashion” (Kaplan and Haenlein, 2010). This network also allowed the creation of User Generated Content (UGC), which is a concept that outlines diverse “forms of media content that are publicly available and created by end-users” (Kaplan and Haenlein, 2010). As a result, there has been an increasing introduction of online consumer rating and review platforms for many categories of products (e.g. hotels, restaurants, activities, and electronic goods), where consumers come together to share and seek opinions and feelings, information, photos and videos (Wang, 2011).
Online restaurant review websites play an essential role in users' electronic word-of-mouth (e-WoM) and decision-making process due to the great connection and information provided between and for them. For instance, Trip Advisor is an online consumer rating and review platform that provides contact and assistance to users since it offers a variety of filtering services to narrow the search selection, information about the products or services (e.g. location, menus and photos) and comments posted by the consumers about their experience and the “service, food quality, price, (…) on the restaurant” (Yan et al., 2015; Yu and Zhang, 2020).
In light of the above, the present paper aims to provide a better understanding of electronic word-of-mouth (e-WOM) for restaurants and its motivations and how it can influence the behaviour of other consumers, which will be accomplished through the sentiment analysis of reviews in one of the most extensive review platforms regarding gastronomy, the TripAdvisor. Nonetheless, investigating a possible relationship between sentiment and review rating is also one of the goals of this present paper.
Additionally, the present research is organized as follows. Section 2 reviews eWOM and related literature about it, and then it is followed by the methodology description (data collection and analysis) in section 3. After that, section 4 presents the results of the data analyses. Finally, section 5 concludes the paper and suggests future research.
Key Terms in this Chapter
Sentiment Analysis: Through the application of text mining algorithms, it allows analyzing textual data, for example, online reviews, to understand whether the sentiment expressed in the text is positive, negative or neutral.
Online Review: Comments made by visitors about a service or tourist product, shared on social networks or travel websites, that convey a testimony of the experience lived during the visit.
Consumer Behavior: This is the focus of studies of consumers and the processes they employ to choose use (i.e., consume), and dispose of products and services.
Tourism Industry: One of the largest industries in the world. It emerged to satisfy the human need to travel to and see different places as part of the service sector, including hospitality (e.g., accommodation, restaurants), transportation (e.g., airlines, car rental), travel facilitation and information (e.g., tour operators, tourist information centers), and attractions and entertainment (e.g., heritage sites and traditional and cultural events).
Electronic Word-of-Mouth: Shared content on social media that conveys an opinion about a product, which becomes a recommendation for future users looking for testimonials about the same product to support them in the decision-making process.
Tourism Experience: This is a set of activities in which individuals engage on their personal terms, such as pleasant and memorable places, allowing each tourist to build his or her own travel experiences so that these satisfy a wide range of personal needs, from pleasure to a search for meaning.
Social media: These aggregates of online communications channels can be considered tools that can be used to define new business models strategically, taking into consideration analyses of community user-generated content and information shared with other members of these online communities.