5-Star Hotel Customer Satisfaction Analysis Using Hybrid Methodology

TL;DR

This paper presents a hybrid approach combining data mining and natural language processing to analyze online reviews for identifying factors influencing customer satisfaction in the hotel industry.

Contribution

It introduces a novel hybrid methodology that integrates data mining and NLP techniques for more accurate customer satisfaction analysis from review data.

Findings

High accuracy in identifying satisfaction factors

Effective extraction of customer sentiment from reviews

Enhanced understanding of review data impact

Abstract

Due to the rapid development of non-face-to-face services due to the corona virus, commerce through the Internet, such as sales and reservations, is increasing very rapidly. Consumers also post reviews, suggestions, or judgments about goods or services on the website. The review data directly used by consumers provides positive feedback and nice impact to consumers, such as creating business value. Therefore, analysing review data is very important from a marketing point of view. Our research suggests a new way to find factors for customer satisfaction through review data. We applied a method to find factors for customer satisfaction by mixing and using the data mining technique, which is a big data analysis method, and the natural language processing technique, which is a language processing method, in our research. Unlike many studies on customer satisfaction that have been conducted in the past, our research has a novelty of the thesis by using various techniques. And as a result of the analysis, the results of our experiments were very accurate.