Abstract
The goal of the current research is to investigate the link between the emotional aspects of hotel and travel organization customers' reviews and their normative (e.g., star rating) rankings. After filtering, the Yelp dataset generated 3,47,803 hotel and travel company reviews. Following the purification of user reviews, we used an unsupervised machine learning technique-based NRC Emotion Lexicon to study the relationships between various emotional aspects of reviews and their normative values (e.g., star rating) for the review. Customers express different sorts of feelings for different types of emotional aspects, forcing them to assign different stars, according to the study's findings. The study is the first to use a lexicon-based unsupervised learning approach to look into the emotional aspects of hotel and travel organization reviews and associated normative (e.g., star rating) rankings.
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