Latent Aspect Detection from Online Unsolicited Customer Reviews
This addresses the challenge for product owners and service providers in identifying hidden customer feedback to reduce churn, though it is incremental as it builds on existing unsupervised techniques.
The paper tackles the problem of detecting latent aspects in online customer reviews, where existing supervised methods fail, by proposing an unsupervised method based on a two-stage generative process and latent Dirichlet allocation, achieving state-of-the-art improvements on benchmark datasets.
Within the context of review analytics, aspects are the features of products and services at which customers target their opinions and sentiments. Aspect detection helps product owners and service providers to identify shortcomings and prioritize customers' needs, and hence, maintain revenues and mitigate customer churn. Existing methods focus on detecting the surface form of an aspect by training supervised learning methods that fall short when aspects are latent in reviews. In this paper, we propose an unsupervised method to extract latent occurrences of aspects. Specifically, we assume that a customer undergoes a two-stage hypothetical generative process when writing a review: (1) deciding on an aspect amongst the set of aspects available for the product or service, and (2) writing the opinion words that are more interrelated to the chosen aspect from the set of all words available in a language. We employ latent Dirichlet allocation to learn the latent aspects distributions for generating the reviews. Experimental results on benchmark datasets show that our proposed method is able to improve the state of the art when the aspects are latent with no surface form in reviews.