Sentiment Classification of Post-Earthquake Consumer Brand Hate on Social Media Using Machine Learning Techniques

Year 2024, Volume: 6 Issue: 1, 58 - 69, 13.03.2024

Esra Erarslan Şahver Omerakı Çekirdekci

https://doi.org/10.58307/kaytek.1387979

Abstract

The widespread use of social media allows consumers to evaluate brands and to get into a direct interaction with brands and other followers of the same brands. After the devastating earthquake on February 6th, 2023, in ten provinces in Turkey a social media brand hatred was observed on two global brands Netflix and Starbucks. Brands were accused of not showing the necessary sensitivity and empathy towards the affected and the brand devotees. The objective of this study is to examine and classify brand hatred in online consumer-generated content using supervised machine learning methods. While the construct of brand hate has been extensively investigated in the discipline of marketing using different data collection methodologies, this is one of the first attempts to use machine learning methods for the analysis of the phenomenon. Unlike classic polarization, the labeling process was associated with the size of brand hatred; 0 denotes neutral reactions, -1 negative emotional reactions, and -2 negative relationship reactions. Support Vector Machines (SVM) was identified as the most successful algorithm for the explanation of the phenomenon.

Keywords

Sentiment analysis, machine learning techniques, brand hate

Doğal Afet Sonrası Yorumların Makine Öğrenmesi Yöntemleri ile Sınıflandırılması

Abstract

Sosyal medya kullanımının yaygınlaşması, tüketicilerin markaları değelendirmesine, markalar ve aynı markanın diğer kullanıcılarına doğrudan etkileşimde bulunmasına olanak sağlamaktadır. 6 Şubat 2023 tarihinde meydana gelen depremin ardından Türkiye’nin on bir ilinde iki küresel marka olan Netflix ve Starbucks’a yönelik marka nefreti gözlemlenmiştir. Her iki marka da depremzedelere ve marka elçilerine gerekli hassasiyeti ve empatiyi göstermemekle suçlandı. Bu çalışmanın amacı, denetimli makine öğrenme yöntemlerini kullanarak tüketici tarafından oluşturulan içeriklerdeki marka nefretini incelemek ve sınıflandırmaktadır. Pazarlama disiplininde marka nefretinin yapısı çeşitli veri toplama yöntemleri ile kapsamlı biçimde araştırılmıştır ancak bu çalışma, marka nefretinin makine öğrenmesi yöntemleri ile incelendiği ilk girişimlerden biridir. Klasik polarizasyon işleminin aksine etiketleme işlemi yorumlardaki duygu yüküne bağlı olarak yapılmıştır; 0 nötr tepkileri, -1 negatif duygusal tepkileri ve -2 negatif ilişkisel tepkileri ifade etmektedir. Analiz sonuçlarına göre Destek Vektör Makineleri (DVM) yöntemi bu fenomenin açıklanmasında en başarılı algoritma olarak bulunmuştur.

Keywords

Duygu analizi, makine öğrenmesi yöntemleri, marka nefreti.

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