# Clustering Airbnb Reviews

## TL;DR

This paper presents a novel clustering method for Airbnb reviews that identifies meaningful consumer segments based on underlying topics and sentiments, using a mixture of latent variable models tailored for binary review data.

## Contribution

It introduces a penalized mixture of latent traits model with component-specific rate parameters for effective clustering of binary review data, revealing distinct guest segments.

## Key findings

- Identified four distinct guest segments based on review content and sentiment.
- Developed a mixture model that reduces parameters and improves clustering accuracy.
- Applied the method to Boston Airbnb reviews from 2009-2016.

## Abstract

In the last decade, online customer reviews increasingly exert influence on consumers' decision when booking accommodation online. The renewal importance to the concept of word-of mouth is reflected in the growing interests in investigating consumers' experience by analyzing their online reviews through the process of text mining and sentiment analysis. A clustering approach is developed for Boston Airbnb reviews submitted in the English language and collected from 2009 to 2016. This approach is based on a mixture of latent variable models, which provides an appealing framework for handling clustered binary data. We address here the problem of discovering meaningful segments of consumers that are coherent from both the underlying topics and the sentiment behind the reviews. A penalized mixture of latent traits approach is developed to reduce the number of parameters and identify variables that are not informative for clustering. The introduction of component-specific rate parameters avoids the over-penalization that can occur when inferring a shared rate parameter on clustered data. We divided the guests into four groups -- property driven guests, host driven guests, guests with recent overall negative stay and guests with some negative experiences.