EXTRACTING LATENT NEEDS FROM ONLINE REVIEWS THROUGH DEEP LEARNING BASED LANGUAGE MODEL Article Swipe
Yi Han
,
Ryan Bruggeman
,
Joseph J. Peper
,
Estefania Ciliotta Chehade
,
Tucker J. Marion
,
Paolo Ciuccarelli
,
Mohsen Moghaddam
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1017/pds.2023.186
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1017/pds.2023.186
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.
Related Topics
Concepts
Sentiment analysis
Computer science
Task (project management)
Process (computing)
Generative grammar
Scalability
Product (mathematics)
Artificial intelligence
Service (business)
Generative model
Natural language processing
Data science
Marketing
Management
Operating system
Mathematics
Database
Business
Economics
Geometry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1017/pds.2023.186
- https://www.cambridge.org/core/services/aop-cambridge-core/content/view/B52EABEF34AB44A3BD3754F62EA5E5E2/S2732527X23001864a.pdf/div-class-title-extracting-latent-needs-from-online-reviews-through-deep-learning-based-language-model-div.pdf
- OA Status
- hybrid
- Cited By
- 6
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4381185329
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4381185329Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1017/pds.2023.186Digital Object Identifier
- Title
-
EXTRACTING LATENT NEEDS FROM ONLINE REVIEWS THROUGH DEEP LEARNING BASED LANGUAGE MODELWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-06-19Full publication date if available
- Authors
-
Yi Han, Ryan Bruggeman, Joseph J. Peper, Estefania Ciliotta Chehade, Tucker J. Marion, Paolo Ciuccarelli, Mohsen MoghaddamList of authors in order
- Landing page
-
https://doi.org/10.1017/pds.2023.186Publisher landing page
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-
https://www.cambridge.org/core/services/aop-cambridge-core/content/view/B52EABEF34AB44A3BD3754F62EA5E5E2/S2732527X23001864a.pdf/div-class-title-extracting-latent-needs-from-online-reviews-through-deep-learning-based-language-model-div.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://www.cambridge.org/core/services/aop-cambridge-core/content/view/B52EABEF34AB44A3BD3754F62EA5E5E2/S2732527X23001864a.pdf/div-class-title-extracting-latent-needs-from-online-reviews-through-deep-learning-based-language-model-div.pdfDirect OA link when available
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Sentiment analysis, Computer science, Task (project management), Process (computing), Generative grammar, Scalability, Product (mathematics), Artificial intelligence, Service (business), Generative model, Natural language processing, Data science, Marketing, Management, Operating system, Mathematics, Database, Business, Economics, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 3, 2023: 1Per-year citation counts (last 5 years)
- References (count)
-
39Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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