Analyzing Consumer Reviews for Understanding Drivers of Hotels Ratings: An Indian Perspective
This provides insights for the hospitality industry in India to improve services based on consumer feedback, but it is incremental as it applies standard methods to a new dataset.
The study analyzed Indian hotel reviews to identify which aspects most influence consumer ratings, using topic modeling and sentiment analysis to extract aspects and Random Forest to determine their predictive importance for ratings.
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.