EXTRACTING LATENT NEEDS FROM ONLINE REVIEWS THROUGH DEEP LEARNING BASED LANGUAGE MODEL

Abstract

AbstractAspect-based sentiment analysis (ABSA) provides an opportunity to systematically generate user's opinions of specific aspects to enrich the idea creation process in the early stage of product/service design process. Yet, the current ABSA task has two major limitations. First, existing research mostly focusing on the subsets of ABSA task, e.g. aspect-sentiment extraction, extract aspect, opinion, and sentiment in a unified model is still an open problem. Second, the implicit opinion and sentiment are ignored in the current ABSA task. This article tackles these gaps by (1) creating a new annotated dataset comprised of five types of labels, including aspect, category, opinion, sentiment, and implicit indicator (ACOSI) and (2) developing a unified model which could extract all five types of labels simultaneously in a generative manner. Numerical experiments conducted on the manually labeled dataset originally scraped from three major e-Commerce retail stores for apparel and footwear products indicate the performance, scalability, and potentials of the framework developed. Several directions are provided for future exploration in the area of automated aspect-based sentiment analysis for user-centered design.