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A Text Analytics Framework for Performance Assessment and Weakness Detection From Online Reviews

A Text Analytics Framework for Performance Assessment and Weakness Detection From Online Reviews

Amit Singh, Mamata Jenamani, Jitesh Thakkar, Yogesh K. Dwivedi
Copyright: © 2022 |Volume: 30 |Issue: 8 |Pages: 26
ISSN: 1062-7375|EISSN: 1533-7995|EISBN13: 9781668435717|DOI: 10.4018/JGIM.304069
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MLA

Singh, Amit, et al. "A Text Analytics Framework for Performance Assessment and Weakness Detection From Online Reviews." JGIM vol.30, no.8 2022: pp.1-26. http://doi.org/10.4018/JGIM.304069

APA

Singh, A., Jenamani, M., Thakkar, J., & Dwivedi, Y. K. (2022). A Text Analytics Framework for Performance Assessment and Weakness Detection From Online Reviews. Journal of Global Information Management (JGIM), 30(8), 1-26. http://doi.org/10.4018/JGIM.304069

Chicago

Singh, Amit, et al. "A Text Analytics Framework for Performance Assessment and Weakness Detection From Online Reviews," Journal of Global Information Management (JGIM) 30, no.8: 1-26. http://doi.org/10.4018/JGIM.304069

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Abstract

Present research proposes a framework that integrates aspect-level sentiment analysis with multi-criteria decision making (TOPSIS) and control charts to uncover hidden quality patterns. While sentiment analysis quantifies consumer opinions corresponding to various product features, TOPSIS uses the sentiment scores to rank manufacturers based on their relative performance. Finally, U and P control charts assist in discovering the weak aspects and corresponding attributes. To extract aspect-level sentiments from reviews, we developed the ontology of passenger cars and designed a heuristic that connects the opinion-bearing texts to the exact automobile attribute. The proposed framework was applied to a review dataset collected from a well-known car portal in India. Considering five manufacturers from the mid-size car segment, we identified the weakest and discovered the aspects and attributes responsible for its perceived weakness.