Abstract
Companies pay close attention to how consumers react on social media to their products or services. Our work focuses on the identification of Consumer Cynicism, defined as a negative attitude that can have a broad or specific focus and comprises cognitive, affective, and behavioral components. We create a corpus of 619 Spanish-language comments on YouTube car reviews, annotated for four cynicism constructs: Dissatisfaction, Alienation, Skepticism, and Hostility. We compare different classification formulations (binary vs. multi-label) and different pre-trained models (Spanish BETO vs. multilingual BERT). We find binary classifiers derived from BETO consistently outperform multi-label classifiers and classifiers derived from BERT. Our best models achieve F1 of 0.83 for Dissatisfaction, 0.77 for Hostility, 0.71 for Skepticism and 0.70 for Alienation.
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