Analyzing Consumer Reviews for Understanding Drivers of Hotels Ratings: An Indian Perspective

TL;DR

This paper analyzes Indian hotel reviews to identify key factors influencing customer ratings by applying topic modeling, sentiment analysis, and machine learning techniques to understand consumer decision drivers.

Contribution

It introduces a comprehensive approach combining web scraping, topic modeling, sentiment analysis, and Random Forest to uncover and predict rating determinants in hotel reviews.

Findings

Identified key aspects influencing hotel ratings.

Demonstrated the effectiveness of machine learning in predicting ratings.

Provided insights into consumer decision-making in the Indian hospitality sector.

Abstract

In the internet era, almost every business entity is trying to have its digital footprint in digital media and other social media platforms. For these entities, word of mouse is also very important. Particularly, this is quite crucial for the hospitality sector dealing with hotels, restaurants etc. Consumers do read other consumers reviews before making final decisions. This is where it becomes very important to understand which aspects are affecting most in the minds of the consumers while giving their ratings. The current study focuses on the consumer reviews of Indian hotels to extract aspects important for final ratings. The study involves gathering data using web scraping methods, analyzing the texts using Latent Dirichlet Allocation for topic extraction and sentiment analysis for aspect-specific sentiment mapping. Finally, it incorporates Random Forest to understand the importance of the aspects in predicting the final rating of a user.