IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i15p4235-d254992.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/15/4235/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/15/4235/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Constantin Bratianu & Shahrazad Hadad & Ruxandra Bejinaru, 2020. "Paradigm Shift in Business Education: A Competence-Based Approach," Sustainability, MDPI, vol. 12(4), pages 1-17, February.
    2. 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.
    3. Jose Ramon Saura & Pedro Palos-Sanchez & Beatriz Rodríguez Herráez, 2020. "Digital Marketing for Sustainable Growth: Business Models and Online Campaigns Using Sustainable Strategies," Sustainability, MDPI, vol. 12(3), pages 1-5, January.
    4. Gliceria Gómez-Ceballos & Sandys Menoya-Zayas & Juan Pablo Vázquez-Loaiza, 2023. "ICT as a Support for Value Chain Management in Tourism Destinations: The Case of the City of Cuenca, Ecuador," Sustainability, MDPI, vol. 15(13), pages 1-30, June.
    5. 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.
    6. Shrestha, Yash Raj & Krishna, Vaibhav & von Krogh, Georg, 2021. "Augmenting organizational decision-making with deep learning algorithms: Principles, promises, and challenges," Journal of Business Research, Elsevier, vol. 123(C), pages 588-603.
    7. Shaojie Liu & Jing Teng & Yue Gong, 2020. "Extraction Method and Integration Framework for Perception Features of Public Opinion in Transportation," Sustainability, MDPI, vol. 13(1), pages 1-17, December.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yucheng Zhang & Zhiling Wang & Lin Xiao & Lijun Wang & Pei Huang, 2023. "Discovering the evolution of online reviews: A bibliometric review," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-22, December.
    2. Yabing Jiang & Hong Guo, 2012. "Design of Consumer Review Systems and Product Pricing," Working Papers 12-10, NET Institute.
    3. Hyunwoo Hwangbo & Jonghyuk Kim, 2019. "A Text Mining Approach for Sustainable Performance in the Film Industry," Sustainability, MDPI, vol. 11(11), pages 1-16, June.
    4. Kun Chen & Peng Luo & Huaiqing Wang, 2017. "Investigating transitive influences on WOM: from the product network perspective," Electronic Commerce Research, Springer, vol. 17(1), pages 149-167, March.
    5. Daniel Kaimann & Joe Cox, 2014. "The Interaction of Signals: A Fuzzy set Analysis of the Video Game Industry," Working Papers Dissertations 13, Paderborn University, Faculty of Business Administration and Economics.
    6. Daniel Kaimann & Joe Cox, 2014. "The Interaction of Signals: A Fuzzy set Analysis of the Video Game Industry," Working Papers CIE 84, Paderborn University, CIE Center for International Economics.
    7. Khim-Yong Goh & Cheng-Suang Heng & Zhijie Lin, 2013. "Social Media Brand Community and Consumer Behavior: Quantifying the Relative Impact of User- and Marketer-Generated Content," Information Systems Research, INFORMS, vol. 24(1), pages 88-107, March.
    8. Dominik Gutt & Jürgen Neumann & Steffen Zimmermann & Dennis Kundisch & Jianqing Chen, 2018. "Design of Review Systems - A Strategic Instrument to shape Online Review Behavior and Economic Outcomes," Working Papers Dissertations 42, Paderborn University, Faculty of Business Administration and Economics.
    9. Pauwels, Koen & Aksehirli, Zeynep & Lackman, Andrew, 2016. "Like the ad or the brand? Marketing stimulates different electronic word-of-mouth content to drive online and offline performance," International Journal of Research in Marketing, Elsevier, vol. 33(3), pages 639-655.
    10. Christian Fieseler & Matthes Fleck & Miriam Meckel, 2010. "Corporate Social Responsibility in the Blogosphere," Journal of Business Ethics, Springer, vol. 91(4), pages 599-614, February.
    11. Pan, Lee-Yun & Chiou, Jyh-Shen, 2011. "How Much Can You Trust Online Information? Cues for Perceived Trustworthiness of Consumer-generated Online Information," Journal of Interactive Marketing, Elsevier, vol. 25(2), pages 67-74.
    12. Williams, Martin & Buttle, Francis, 2011. "The Eight Pillars of WOM management: Lessons from a multiple case study," Australasian marketing journal, Elsevier, vol. 19(2), pages 85-92.
    13. Jehoshua Eliashberg & Anita Elberse & Mark A.A.M. Leenders, 2006. "The Motion Picture Industry: Critical Issues in Practice, Current Research, and New Research Directions," Marketing Science, INFORMS, vol. 25(6), pages 638-661, 11-12.
    14. Ahmet Bayraktar & Emine Erdogan, 2015. "What Makes Offline Word-Of-Mouth More Influential Than Online Word-Of-Mouth?," Global Journal of Business Research, The Institute for Business and Finance Research, vol. 9(2), pages 95-107.
    15. Hossin Md Altab & Mu Yinping & Hosain Md Sajjad & Adasa Nkrumah Kofi Frimpong & Michelle Frempomaa Frempong & Stephen Sarfo Adu-Yeboah, 2022. "Understanding Online Consumer Textual Reviews and Rating: Review Length With Moderated Multiple Regression Analysis Approach," SAGE Open, , vol. 12(2), pages 21582440221, June.
    16. Supratim Kundu & Swapnajit Chakraborti, 2022. "A comparative study of online consumer reviews of Apple iPhone across Amazon, Twitter and MouthShut platforms," Electronic Commerce Research, Springer, vol. 22(3), pages 925-950, September.
    17. Yubo Chen & Jinhong Xie, 2008. "Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix," Management Science, INFORMS, vol. 54(3), pages 477-491, March.
    18. Koji Ishida & Lisa Slevitch & Katia Siamionava, 2016. "The Effects of Traditional and Electronic Word-of-Mouth on Destination Image: A Case of Vacation Tourists Visiting Branson, Missouri," Administrative Sciences, MDPI, vol. 6(4), pages 1-17, September.
    19. Marchand, André & Hennig-Thurau, Thorsten & Wiertz, Caroline, 2017. "Not all digital word of mouth is created equal: Understanding the respective impact of consumer reviews and microblogs on new product success," International Journal of Research in Marketing, Elsevier, vol. 34(2), pages 336-354.
    20. Camille Alloing & Marie Haikel-Elsabeh, 2012. "Les leaders d'opinion sur les réseaux socionumériques," Post-Print hal-00958659, HAL.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:11:y:2019:i:15:p:4235-:d:254992. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.