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Online Product Reviews and Their Impact on Third Party Sellers Using Natural Language Processing

Author

Listed:
  • Akash Phaniteja Nellutla

    (Symbiosis Centre For Management and HRD, Symbiosis International University (Deemed), India)

  • Manoj Hudnurkar

    (Symbiosis Centre For Management and HRD, Symbiosis International University (Deemed), India)

  • Suhas Suresh Ambekar

    (Symbiosis Centre For Management and HRD, Symbiosis International University (Deemed), India)

  • Abhay D. Lidbe

    (Alabama Transportation Institute, University of Alabama, USA)

Abstract

The purpose of this paper is to gain insights from the online product reviews of e-commerce sites such as Flipkart and Amazon and analyze its impact on third party sellers. To judge the authenticity of a product, reviews are more useful than ratings, since ratings do not give a complete picture. It is always preferred to consider both the product and seller reviews to have a seamless delivery and defect less product. In this paper, natural processing methods are used to gain insights by considering online reviews of a product. Methods such as sentiment analysis, bag of words model help to understand the impact of online product reviews on the seller's ratings and their performance over some time. The reviews are categorized into positive, negative, and neutral using sentiment analysis. Further, topic modeling is done to find out the topic reviews are majorly referring to. The seller reviews for a specific product after analysis are compared with the overall seller reviews to judge the authenticity. The results of this paper would be beneficial to both the consumers and sellers.

Suggested Citation

  • Akash Phaniteja Nellutla & Manoj Hudnurkar & Suhas Suresh Ambekar & Abhay D. Lidbe, 2021. "Online Product Reviews and Their Impact on Third Party Sellers Using Natural Language Processing," International Journal of Business Intelligence Research (IJBIR), IGI Global, vol. 12(1), pages 26-47, January.
    • Handle: RePEc:igg:jbir00:v:12:y:2021:i:1:p:26-47
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    References listed on IDEAS

    as
    1. Ifie, Kemefasu, 2020. "Excellent Product … But Too Early to Say: Consumer Reactions to Tentative Product Reviews," Journal of Interactive Marketing, Elsevier, vol. 52(C), pages 35-51.
    2. Naoshi Doi & Hitoshi Hayakawa, 2020. "Electronic word-of-mouth: a survey from an economics perspective," International Journal of the Economics of Business, Taylor & Francis Journals, vol. 27(2), pages 303-320, May.
    3. M. Huang & A. D. Pape, 2020. "The Impact of Online Consumer Reviews on Online Sales: The Case-Based Decision Theory Approach," Journal of Consumer Policy, Springer, vol. 43(3), pages 463-490, September.
    4. Conor Gallagher & Eoghan Furey & Kevin Curran, 2019. "The Application of Sentiment Analysis and Text Analytics to Customer Experience Reviews to Understand What Customers Are Really Saying," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 15(4), pages 21-47, October.
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