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Sustainable Brand Reputation: Evaluation of iPhone Customer Reviews with Machine Learning and Sentiment Analysis

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  • Mehmet Kayakuş

    (Department of Management Information Systems, Faculty of Social and Human Sciences, Akdeniz University, Antalya 07070, Türkiye)

  • Fatma Yiğit Açikgöz

    (Department of Marketing and Advertising, Social Sciences Vocational School, Akdeniz University, Antalya 07070, Türkiye)

  • Mirela Nicoleta Dinca

    (Department of Biotechnical Systems, Faculty of Biotechnical Systems Engineering, National University of Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania)

  • Onder Kabas

    (Department of Machine, Technical Science Vocational School, Akdeniz University, Antalya 07070, Türkiye)

Abstract

Brand reputation directly influences customer trust and decision-making. A good reputation can lead to greater customer loyalty, commitment, and advocacy. This study aims to understand the effects of brand reputation on customer trust and loyalty and to determine how brands can protect their reputation. This study, which was conducted on the iPhone 11 sample by obtaining statistical data from customer reviews, can be adapted and used by researchers and companies that want to measure brand reputation. In this study, customer reviews for the iPhone 11 phone on the Trendyol e-commerce site, the largest e-commerce platform in Turkey, are analyzed using sentiment analysis and machine learning methods. While 85 percent of customers are satisfied with the iPhone 11, 13 percent are dissatisfied with it. The neutral comment rate of 2 percent indicates that some customers do not express a clear positive or negative opinion about the product. In the comments of customers who bought the iPhone 11, there are those who express satisfaction with the quality, technical features, performance, and price/performance ratio of the product, as well as those who express significant complaints about delivery, quality, price, and customer service. Neutral comments generally focus on the product itself, price, quality, shipping, and packaging, and make informative evaluations. A sustainable reputation is based on the extent to which an organization embraces ethical principles, social responsibility, and sustainable practices throughout its operations and business relationships. Brands can improve, protect, and increase their brand reputation by considering and analyzing the thoughts and feelings of their customers. For this, they should develop policies and strategies to reinforce their strong features and improve their faulty and deficient features.

Suggested Citation

  • Mehmet Kayakuş & Fatma Yiğit Açikgöz & Mirela Nicoleta Dinca & Onder Kabas, 2024. "Sustainable Brand Reputation: Evaluation of iPhone Customer Reviews with Machine Learning and Sentiment Analysis," Sustainability, MDPI, vol. 16(14), pages 1-17, July.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:14:p:6121-:d:1437325
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    References listed on IDEAS

    as
    1. Roy, Atin & Chakraborty, Subrata, 2023. "Support vector machine in structural reliability analysis: A review," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    2. Mehmet Kayakuş, 2020. "The Estimation of Turkey's Energy Demand Through Artificial Neural Networks and Support Vector Regression Methods," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 8(2), pages 227-236, December.
    3. Mehmet Kayakuş & Fatma Yiğit Açıkgöz, 2022. "Classification of News Texts by Categories Using Machine Learning Methods," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 10(2), pages 155-166, December.
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    1. Fatma Yiğit Açikgöz & Mehmet Kayakuş & Bianca-Ștefania Zăbavă & Onder Kabas, 2024. "Brand Reputation and Trust: The Impact on Customer Satisfaction and Loyalty for the Hewlett-Packard Brand," Sustainability, MDPI, vol. 16(22), pages 1-17, November.
    2. Fatma Yiğit Açikgöz & Mehmet Kayakuş & Georgiana Moiceanu & Nesrin Sönmez, 2024. "A New Approach to Assess Sustainable Corporate Reputation with Citizen Comments Using Machine Learning and Natural Language Processing," Sustainability, MDPI, vol. 16(22), pages 1-19, November.

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