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• W3118869673 abstract "Devising intelligent systems capable of identifying the idiosyncratic needs of users at scale and translating them into attribute-level design feedback and recommendations is a key prerequisite for successful user-centered design processes. Recent studies show that 49% of design firms lack systems and tools for monitoring external platforms, and only 8% have adopted digital, data-driven approaches for new product development despite acknowledging them as a high priority. The state-of-the-art attribute-level sentiment analysis approaches based on deep learning have achieved promising results; however, these methods pose strict preconditions, require manually labeled data for training and pre-defined attributes by experts, and only classify sentiments intro predefined categories which have limited implications for designers. This article develops a rule-based methodology for extracting and analyzing the sentiment expressions of users on a large scale, from myriad reviews available on social media and e-commerce platforms. The methodology further advances current unsupervised attribute-level sentiment analysis approaches by enabling efficient identification and mapping of sentiment expressions of individual users onto their respective attributes. Experiments on a large dataset scraped from a major e-commerce retail store for apparel and indicate 74.3%–93.8% precision in extracting attribute-level sentiment expressions of users and demonstrate the feasibility and potentials of the developed methodology for large-scale need finding from user reviews." @default.
• W3118869673 created "2021-01-18" @default.
• W3118869673 creator A5017863793 @default.
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• W3118869673 date "2021-06-01" @default.
• W3118869673 modified "2023-10-12" @default.
• W3118869673 title "Analysis of sentiment expressions for user-centered design" @default.
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• W3118869673 doi "https://doi.org/10.1016/j.eswa.2021.114604" @default.
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