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Analyzing Online Car Reviews Using Text Mining

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  • En-Gir Kim

    (Department of Business Administration, Seoul National University of Science and Technology, 232 Gongreung-Ro, Nowon-Gu, Seoul 01811, Korea)

  • Se-Hak Chun

    (Department of Business Administration, Seoul National University of Science and Technology, 232 Gongreung-Ro, Nowon-Gu, Seoul 01811, Korea)

Abstract

Consumer reviews on the web have rapidly become an important information source through which consumers can share their experiences and opinions about products and services. It is a form of text-based communication that provides new possibilities and opens vast perspectives in terms of marketing. Reading consumer reviews gives marketers an opportunity to eavesdrop on their own consumers. This paper examines consumer reviews of three different competitive automobile brands and analyzes the advantages and disadvantages of each vehicle using text mining and association rule methods. The data were collected from an online resource for automotive information, Edmunds.com, with a scraping tool “ParseHub” and then processed in R software for statistical computing and graphics. The paper provides detailed insights into the superior and problematic sides of each brand and into consumers’ perceptions of automobiles and highlights differences between satisfied and unsatisfied groups regarding the best and worst features of the brands.

Suggested Citation

  • En-Gir Kim & Se-Hak Chun, 2019. "Analyzing Online Car Reviews Using Text Mining," Sustainability, MDPI, vol. 11(6), pages 1-22, March.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:6:p:1611-:d:214651
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    References listed on IDEAS

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    Cited by:

    1. Kim, Taeyong & Hwang, Seungsoo & Kim, Minkyung, 2022. "Text analysis of online customer reviews for products in the FCB quadrants: Procedure, outcomes, and implications," Journal of Business Research, Elsevier, vol. 150(C), pages 676-689.
    2. Wang, Chen & Chu, Zhongzhu & Gu, Wei, 2021. "Assessing the role of public attention in China's wastewater treatment: A spatial perspective," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    3. Elena Sánchez-Vargas & Ana María Campón-Cerro & Elvira Prado-Recio & Bárbara Sofía Pasaco-González & Ana Moreno-Lobato, 2022. "Exploring the Hotel Experience in a Cultural City through a UGC Analysis," Sustainability, MDPI, vol. 14(23), pages 1-15, November.
    4. JooSeok Oh & Timothy Paul Connerton & Hyun-Jung Kim, 2019. "The Rediscovery of Brand Experience Dimensions with Big Data Analysis: Building for a Sustainable Brand," Sustainability, MDPI, vol. 11(19), pages 1-21, September.
    5. Bai, Chunguang & Dallasega, Patrick & Orzes, Guido & Sarkis, Joseph, 2020. "Industry 4.0 technologies assessment: A sustainability perspective," International Journal of Production Economics, Elsevier, vol. 229(C).
    6. Vasile-Daniel Păvăloaia & Elena-Mădălina Teodor & Doina Fotache & Magdalena Danileţ, 2019. "Opinion Mining on Social Media Data: Sentiment Analysis of User Preferences," Sustainability, MDPI, vol. 11(16), pages 1-21, August.

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