Asymmetric effect of feature level sentiment on product rating: an application of bigram natural language processing (NLP) analysis
ISSN: 1066-2243
Article publication date: 30 July 2021
Issue publication date: 9 May 2022
Abstract
Purpose
The evaluation of perceived attribute performance reflected in online consumer reviews (OCRs) is critical in gaining timely marketing insights. This study proposed a text mining approach to measure consumer sentiments at the feature level and their asymmetric impacts on overall product ratings.
Design/methodology/approach
This study employed 49,130 OCRs generated for 14 wireless earbud products on Amazon.com. Word combinations of the major quality dimensions and related sentiment words were identified using bigram natural language processing (NLP) analysis. This study combined sentiment dictionaries and feature-related bigrams and measured feature level sentiment scores in a review. Furthermore, the authors examined the effect of feature level sentiment on product ratings.
Findings
The results indicate that customer sentiment for product features measured from text reviews significantly and asymmetrically affects the overall rating. Building upon the three-factor theory of customer satisfaction, the key quality dimensions of wireless earbuds are categorized into basic, excitement and performance factors.
Originality/value
This study provides a novel approach to assess customer feature level evaluation of a product and its impact on customer satisfaction based on big data analytics. By applying the suggested methodology, marketing managers can gain in-depth insights into consumer needs and reflect this knowledge in their future product or service improvement.
Keywords
Acknowledgements
This paper forms part of a special section “Digital Transformation and Consumer Experience”, guest edited by Dong-Mo Koo, Jungkeun Kim and Taewan Kim.
The submission is for the special issue of “Digital Transformation and Consumer Experience” through the ICAMA 2020 conference.
Citation
Oh, Y.K. and Yi, J. (2022), "Asymmetric effect of feature level sentiment on product rating: an application of bigram natural language processing (NLP) analysis", Internet Research, Vol. 32 No. 3, pp. 1023-1040. https://doi.org/10.1108/INTR-11-2020-0649
Publisher
:Emerald Publishing Limited
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