Latent Aspect Detection from Online Unsolicited Customer Reviews

• URL: http://arxiv.org/abs/2204.06964v1
• Date: Thu, 14 Apr 2022 13:46:25 GMT
• Title: Latent Aspect Detection from Online Unsolicited Customer Reviews
• Authors: Mohammad Forouhesh, Arash Mansouri, Hossein Fani
• Abstract summary: Aspect detection helps product owners and service providers to identify shortcomings and prioritize customers' needs. 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. We propose an unsupervised method to extract latent occurrences of aspects.
• Score: 3.622430080512776
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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