Clustering halal food consumers: A Twitter sentiment analysis

Abstract

The exponential growth of user-generated social media content raises the possibility of using opinion mining techniques in tracking and monitoring consumers’ preferences. Although the web represents a valuable data mining source about consumers’ opinions, no previous studies have investigated halal food sentiments expressed on social media. In this study, we fill this research gap by analyzing a random sample of 3,919 halal food tweets. A 6,800 seed adjectives expert-predefined lexicon was used to conduct the analysis. A generally positive sentiment toward halal food was detected through descriptive statistical analysis, whereas partitioning around medoids (PAM) clustering indicated that it is possible to cluster halal food consumers into four distinct segments. Thus, it seems that halal food consumers represent a highly heterogeneous group, divisible by level of religiosity, self-identity, animal welfare attitudes, and concern for food authenticity.