Using Intelligent Text Analysis of Online Reviews to Determine the Main Factors of Restaurant Value Propositions

Abstract

This chapter discusses the sentiment classification of text messages containing customer reviews of an online restaurant service system using machine-learning methods, in particular text mining and multivariate text sentiment analysis. The study determines the structure of value proposition factors based on online restaurant reviews on TripAdvisor, collecting information on consumer preferences and the restaurant services in St. Petersburg (Russia) quality assessment and examines the influence of service format and reviews tonality on ratings restaurants factors. The service format context is proposed as the main attribute influencing the formation of the restaurant business value proposition and of relevance for online reviews. The results showed the key factors in the study of the sentiment were cuisine and dishes, reviews and ratings, and targeted search. MANOVA analysis represented that for special offers and features, reviews and ratings, factors and quantitative star ratings influenced the negative and positive sentiment of online reviews significantly.