A thousand ways to say 'Delicious!'—Categorizing expressions of deliciousness from restaurant reviews on the social network site Yelp

How does the consumer choose a restaurant? An overview of the determinants of consumer satisfaction

The failure rate of restaurants is high in many countries, primarily because of the complex relationships between services and customers. Therefore, improving restaurant customer experience is a significant challenge for entrepreneurs. This multi-dimensional experience encompasses several aspects that may or may not be related to food consumption. Many restaurant owners can avoid bankruptcy if they understand theories of service quality and the factors involved. The objective of this research is to identify and summarize known important factors that lead consumers to choose, patronize or be satisfied with a restaurant. The research question for this review is: What are the important factors that influence consumers (population) to choose, patronize, or be satisfied with a restaurant  (outcome)? Therefore, we conducted an integrative review to address this question. We included 111 studies and identified 117 factors/indicators related to consumer satisfaction and restaurant choices. First, we grouped these factors into four categories based on the Big Four restaurant attributes: atmosphere, food, service, and price & value. Four categories emerged based on consumer- and business-related aspects: behavioral intentions, values and beliefs, experiences, and companies. The "food" category is the most important factor in consumer choice and experience. However, the importance of this category may vary depending on the situation (e.g., lunch, dinner, weekends, weekdays) and should be carefully considered, as all categories were relevant but intricate. Such factors are associated with many positive outcomes, such as satisfaction, loyalty, brand love, patronization, and intent to visit and revisit.

A critical review of social media research in sensory-consumer science

The collection and analysis of digital data from social media is a rapidly growing methodology in sensory-consumer science, with a wide range of applications for research studying consumer attitudes, preferences, and sensory responses to food. The aim of this review article was to critically evaluate the potential of social media research in sensory-consumer science with a focus on advantages and disadvantages. This review began with an exploration into different sources of social media data and the process by which data from social media is collected, cleaned, and analyzed through natural language processing for sensory-consumer research. It then investigated in detail the differences between social media-based and conventional methodologies, in terms of context, sources of bias, the size of data sets, measurement differences, and ethics. Findings showed participant biases are more difficult to control using social media approaches, and precision is inferior to conventional methods. However, findings also showed social media methodologies may have other advantages including an increased ability to investigate trends over time and easier access to cross-cultural or global insights. Greater research in this space will identify when social media can best function as an alternative to conventional methods, and/or provide valuable complementary information.

Topic Modeling and Sentiment Analysis of Yelp Restaurant Reviews

The use of online consumer reviews (OCRs) websites like Yelp or TripAdvisor has increasingly gained popularity over the years. However, many products or services now have a large number of online reviews, which makes it difficult for consumers to decide which reviews to pay attention to, or for businesses to identify which critical areas to improve on. The use of text mining and sentiment mining techniques is deemed important to automatically process the content of online reviews and help improve review usefulness. Applying a four-phase research model, our study demonstrated data extraction and cleaning, topic modeling using Latent Dirichlet allocation (LDA) to extract five topics (Price, Time, Food, Service, and Location), sentiment analysis using Python TextBlob to aggregate consumer sentiment per topic from a Yelp restaurant reviews dataset, and model performance evaluation. We proposed that the design of recommender systems for OCRs or business decision-making systems can be faster, simpler, and more useful by integrating automatic topics extraction and sentiment analysis.

Utilization of text mining as a big data analysis tool for food science and nutrition

Big data analysis has found applications in many industries due to its ability to turn huge amounts of data into insights for informed business and operational decisions. Advanced data mining techniques have been applied in many sectors of supply chains in the food industry. However, the previous work has mainly focused on the analysis of instrument-generated data such as those from hyperspectral imaging, spectroscopy, and biometric receptors. The importance of digital text data in the food and nutrition has only recently gained attention due to advancements in big data analytics. The purpose of this review is to provide an overview of the data sources, computational methods, and applications of text data in the food industry. Text mining techniques such as word-level analysis (e.g., frequency analysis), word association analysis (e.g., network analysis), and advanced techniques (e.g., text classification, text clustering, topic modeling, information retrieval, and sentiment analysis) will be discussed. Applications of text data analysis will be illustrated with respect to food safety and food fraud surveillance, dietary pattern characterization, consumer-opinion mining, new-product development, food knowledge discovery, food supply-chain management, and online food services. The goal is to provide insights for intelligent decision-making to improve food production, food safety, and human nutrition.