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Managing Marketing Decision-Making with Sentiment Analysis: An Evaluation of the Main Product Features Using Text Data Mining

Author

Listed:
  • Erick Kauffmann

    (School of Industrial Engineering, University of Costa Rica, San José 11501-2060, Costa Rica)

  • Jesús Peral

    (Department of Software and Computing Systems, University of Alicante, 03690 Alicante, Spain)

  • David Gil

    (Department of Computer Technology and Computation, University of Alicante, 03690 Alicante, Spain)

  • Antonio Ferrández

    (Department of Software and Computing Systems, University of Alicante, 03690 Alicante, Spain)

  • Ricardo Sellers

    (Department of Marketing, University of Alicante, 03690 Alicante, Spain)

  • Higinio Mora

    (Department of Computer Technology and Computation, University of Alicante, 03690 Alicante, Spain)

Abstract

Companies have realized the importance of “big data” in creating a sustainable competitive advantage, and user-generated content (UGC) represents one of big data’s most important sources. From blogs to social media and online reviews, consumers generate a huge amount of brand-related information that has a decisive potential business value for marketing purposes. Particularly, we focus on online reviews that could have an influence on brand image and positioning. Within this context, and using the usual quantitative star score ratings, a recent stream of research has employed sentiment analysis (SA) tools to examine the textual content of reviews and categorize buyer opinions. Although many SA tools split comments into negative or positive, a review can contain phrases with different polarities because the user can have different sentiments about each feature of the product. Finding the polarity of each feature can be interesting for product managers and brand management. In this paper, we present a general framework that uses natural language processing (NLP) techniques, including sentiment analysis, text data mining, and clustering techniques, to obtain new scores based on consumer sentiments for different product features. The main contribution of our proposal is the combination of price and the aforementioned scores to define a new global score for the product, which allows us to obtain a ranking according to product features. Furthermore, the products can be classified according to their positive, neutral, or negative features (visualized on dashboards), helping consumers with their sustainable purchasing behavior. We proved the validity of our approach in a case study using big data extracted from Amazon online reviews (specifically cell phones), obtaining satisfactory and promising results. After the experimentation, we could conclude that our work is able to improve recommender systems by using positive, neutral, and negative customer opinions and by classifying customers based on their comments.

Suggested Citation

  • Erick Kauffmann & Jesús Peral & David Gil & Antonio Ferrández & Ricardo Sellers & Higinio Mora, 2019. "Managing Marketing Decision-Making with Sentiment Analysis: An Evaluation of the Main Product Features Using Text Data Mining," Sustainability, MDPI, vol. 11(15), pages 1-19, August.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:15:p:4235-:d:254992
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    References listed on IDEAS

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    1. Artem Timoshenko & John R. Hauser, 2019. "Identifying Customer Needs from User-Generated Content," Marketing Science, INFORMS, vol. 38(1), pages 1-20, January.
    2. Luigi Dolores & Maria Macchiaroli & Gianluigi De Mare, 2017. "Sponsorship for the Sustainability of Historical-Architectural Heritage: Application of a Model’s Original Test Finalized to Maximize the Profitability of Private Investors," Sustainability, MDPI, vol. 9(10), pages 1-16, September.
    3. Qiang Zhang & Xiaonong Lu & Zhanglin Peng & Minglun Ren, 2019. "Perspective: a review of lifecycle management research on complex products in smart-connected environments," International Journal of Production Research, Taylor & Francis Journals, vol. 57(21), pages 6758-6779, November.
    4. Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2007. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Working Papers 07-36, NET Institute.
    5. David Godes & Dina Mayzlin, 2004. "Using Online Conversations to Study Word-of-Mouth Communication," Marketing Science, INFORMS, vol. 23(4), pages 545-560, June.
    6. Dellarocas, Chrysanthos, 2003. "The Digitization of Word-of-mouth: Promise and Challenges of Online Feedback Mechanisms," Working papers 4296-03, Massachusetts Institute of Technology (MIT), Sloan School of Management.
    7. Chrysanthos Dellarocas, 2003. "The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms," Management Science, INFORMS, vol. 49(10), pages 1407-1424, October.
    8. Alan Pomering & Lester W. Johnson, 2018. "Building Sustainability into Services Marketing: Expanding Decision-Making from a Mix to a Matrix," Sustainability, MDPI, vol. 10(9), pages 1-12, August.
    9. Joya A. Kemper & C. Michael Hall & Paul W. Ballantine, 2019. "Marketing and Sustainability: Business as Usual or Changing Worldviews?," Sustainability, MDPI, vol. 11(3), pages 1-17, February.
    10. Jose Ramon Saura & Pedro Palos-Sanchez & Antonio Grilo, 2019. "Detecting Indicators for Startup Business Success: Sentiment Analysis Using Text Data Mining," Sustainability, MDPI, vol. 11(3), pages 1-14, February.
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    5. Jianhong Luo & Shifen Qiu & Xuwei Pan & Ke Yang & Yuanqingqing Tian, 2022. "Exploration of Spa Leisure Consumption Sentiment towards Different Holidays and Different Cities through Online Reviews: Implications for Customer Segmentation," Sustainability, MDPI, vol. 14(2), pages 1-16, January.
    6. Azra Shamim & Muhammad Ahsan Qureshi & Farhana Jabeen & Misbah Liaqat & Muhammad Bilal & Yalew Zelalem Jembre & Muhammad Attique, 2021. "Multi-Attribute Online Decision-Making Driven by Opinion Mining," Mathematics, MDPI, vol. 9(8), pages 1-25, April.
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    8. Sara Slamić Tarade, 2023. "Discovering the Significance of Sports Footwear Brands through Text Analysis ," GATR Journals jmmr326, Global Academy of Training and Research (GATR) Enterprise.
    9. Beibei Niu & Jinzheng Ren & Ansa Zhao & Xiaotao Li, 2020. "Lender Trust on the P2P Lending: Analysis Based on Sentiment Analysis of Comment Text," Sustainability, MDPI, vol. 12(8), pages 1-14, April.

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