What factors influence the popularity of user-generated text in the
  creative domain? A case study of book reviews
Salim Sazzed
This study investigates a range of psychological, lexical, semantic, and
readability features of book reviews to elucidate the factors underlying their
perceived popularity. To this end, we conduct statistical analyses of various
features, including the types and frequency of opinion and emotion-conveying
terms, connectives, character mentions, word uniqueness, commonness, and
sentence structure, among others. Additionally, we utilize two readability
tests to explore whether reading ease is positively associated with review
popularity. Finally, we employ traditional machine learning classifiers and
transformer-based fine-tuned language models with n-gram features to
automatically determine review popularity. Our findings indicate that, with the
exception of a few features (e.g., review length, emotions, and word
uniqueness), most attributes do not exhibit significant differences between
popular and non-popular review groups. Furthermore, the poor performance of
machine learning classifiers using the word n-gram feature highlights the
challenges associated with determining popularity in creative domains. Overall,
our study provides insights into the factors underlying review popularity and
highlights the need for further research in this area, particularly in the
creative realm.