| Boon et al. (2013) | Online customer reviews (unstructured data) | RapidMiner | The purpose of this research was to perform word frequency analysis on qualitative online comments and customer reviews to measure and quantify hotel service quality |
| Büschken and Allenby (2016) | Online customer reviews (unstructured data) | Sentence-Constrained Latent Dirichlet allocation (SC LDA) model | The research developed a model for text analysis, making use of the sentence structure contained in customer reviews, showing that it leads to improved inference and prediction of consumer ratings of hotels and restaurants |
| Chatterjee et al. (2021) | Online customer reviews (unstructured data) | NRC Word-Emotion Association Lexicon (EmoLex) | Using text-mining, machine-learning and econometric techniques, the purpose of this research was to find which core and augmented service aspects and which emotions are more important in which service contexts in terms of reflecting and predicting customer satisfaction |
| de Haan and Menichelli (2020) | Three different data sources: customer database data (structured data), survey data (structured data), and textual customer data, i.e. reviews, comments on social media, verbal or written customer-firm interactions (unstructured data) | Latent Dirichlet allocation (LDA) | The research investigated the extent to which three different combinations of data sources (including structured and unstructured data), can predict customer churn, showing that the inclusion of unstructured data significantly improves the estimation of customer retention and churn |
| Oh et al. (2022) | Written customer reviews, hotel information and images (unstructured data) | Valence Aware Dictionary and Sentiment Reasoner (VADER) | Using deep learning techniques and approaches, coupled with the theoretical principles of expectation-confirmation theory, the research used unstructured data to predict customer satisfaction in hospitality services |
| Song et al. (2016) | Online customer reviews (unstructured data) | Conducted part-of-speech tagging on customer reviews, whereafter a service-feature word dictionary and sentiment word dictionary was developed with which to measure all the constructs and predict service quality | This study developed an analytic framework and procedures (“customer review-based gap analysis”) to diagnose service quality from online customer reviews |
| Zhu et al. (2021) | Online customer reviews (unstructured data) | Heuristic Processing, Linguistic Feature Analysis, and Deep Learning-based Natural Language Processing (NLP) | This research proposed and tested mechanisms and AI-based technology for defining and identifying the critical online consumer reviews that firms could prioritize to optimize their online response strategies |