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Unsupervised topic labeling and opportunity model of social media data for enhancing automotive product design processes.

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Název: Unsupervised topic labeling and opportunity model of social media data for enhancing automotive product design processes.
Autoři: Hartanto, Broto Widya1 (AUTHOR), Subagyo1 (AUTHOR) subagyo@ugm.ac.id, Budi Dharma, I.G.B.1 (AUTHOR)
Zdroj: Data & Information Management. Dec2025, Vol. 9 Issue 4, pN.PAG-N.PAG. 1p.
Témata: *AUTOMOTIVE engineering, *ENGINEERING design, *MATRIX decomposition, *CLASSIFICATION, *USER-generated content, *STATISTICAL models
Abstrakt: This study introduces a hybrid method combining topic modeling and an opportunity model to generate novel ideas for automobile product design improvements. Furthermore, it proposes a novel unsupervised topic labeling procedure to address the limitations in current topic modeling interpretations, which are often not fully unsupervised. The procedure comprised automatic generation of labels that directly support opportunity modeling and facilitate product design development. To achieve the stated objectives, data was collected from user comments on YouTube car reviews and analyzed using various algorithms and part-of-speech rules, finding that Non-Negative Matrix Factorization with noun-adjective combinations proved most effective in generating comprehensible topic labels and capturing emotional expressions. The results revealed six underserved labels, one served right, and two overserved categories for new vehicle design improvements, providing valuable insights into user experiences. The insights provided in this context are expected to contribute to the potential improvement of vehicle attribute designs, thereby enhancing the efficiency of the entire design process. • The car model changes have accelerated due to market competition and dynamics. • Topic modeling helps explore auto consumer needs from social media comments. • The challenging of interpreting topics requiring unsupervised labeling procedure. • The procedure generates meaningful labels suitable for opportunity analysis input. • Design process efficiency improves with better calculation time and accuracy. [ABSTRACT FROM AUTHOR]
Databáze: Academic Search Index
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Abstrakt:This study introduces a hybrid method combining topic modeling and an opportunity model to generate novel ideas for automobile product design improvements. Furthermore, it proposes a novel unsupervised topic labeling procedure to address the limitations in current topic modeling interpretations, which are often not fully unsupervised. The procedure comprised automatic generation of labels that directly support opportunity modeling and facilitate product design development. To achieve the stated objectives, data was collected from user comments on YouTube car reviews and analyzed using various algorithms and part-of-speech rules, finding that Non-Negative Matrix Factorization with noun-adjective combinations proved most effective in generating comprehensible topic labels and capturing emotional expressions. The results revealed six underserved labels, one served right, and two overserved categories for new vehicle design improvements, providing valuable insights into user experiences. The insights provided in this context are expected to contribute to the potential improvement of vehicle attribute designs, thereby enhancing the efficiency of the entire design process. • The car model changes have accelerated due to market competition and dynamics. • Topic modeling helps explore auto consumer needs from social media comments. • The challenging of interpreting topics requiring unsupervised labeling procedure. • The procedure generates meaningful labels suitable for opportunity analysis input. • Design process efficiency improves with better calculation time and accuracy. [ABSTRACT FROM AUTHOR]
ISSN:25439251
DOI:10.1016/j.dim.2025.100103