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.

本研究通过对书评的心理、词汇、语义、易读性等特征进行统计分析，使用两种易读性测试探索阅读便利程度与书评受欢迎程度的正向关联，并运用传统机器学习分类器和基于 Transformer 的预训练语言模型，自动判断书评的受欢迎程度。研究结果表明，除了一些特征（例如书评长度、情感和词语独特性）之外，大多数属性在受欢迎和不受欢迎的书评组之间没有显著差异。此外，使用词语 N-gram 特征的机器学习分类器的低性能凸显了在判断创意领域受欢迎程度方面所面临的挑战。总体而言，本研究揭示了影响书评受欢迎程度的因素，并强调了进一步研究的需要，尤其是在创意领域。