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

Published online by Cambridge University Press:  19 June 2023

Yi Han ,
Ryan Bruggeman ,
Joseph Peper ,
Estefania Ciliotta Chehade ,
Tucker Marion ,
Paolo Ciuccarelli  and
Mohsen Moghaddam
Yi Han*
Affiliation:
Northeastern university;
Ryan Bruggeman
Affiliation:
Northeastern university;
Joseph Peper
Affiliation:
The University of Michigan
Estefania Ciliotta Chehade
Affiliation:
Northeastern university;
Tucker Marion
Affiliation:
Northeastern university;
Paolo Ciuccarelli
Affiliation:
Northeastern university;
Mohsen Moghaddam
Affiliation:
Northeastern university;
*
Han, Yi, northeastern university, United States of America, [email protected]

Abstract

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Aspect-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.

Keywords

Latent needs findingNatural language processingArtificial intelligenceMachine learningBig data
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 1855 - 1864
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

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