Dataset of Amazon Reader Reviews on Emotional Attachment to Fiction: Evidence from Wild Wise Weird

Reader-book relationships increasingly manifest through digital traces, yet systematic datasets capturing emotional attachment to fiction remain scarce. This paper presents a curated dataset of 129 Amazon reviews for Wild Wise Weird: The Kingfisher Story Collection by Professor Quan-Hoang Vuong, a contemporary Vietnamese fable collection published for global audiences. The dataset bridges psychological constructs (emotional attachment, reading motivation) with computational text analysis through human-coded variables capturing book style descriptors, thematic content, and explicit attachment indicators. Data were collected between December 2024 and January 2025 and include reviews posted between September 2023 and December 2024 across eight countries. The dataset comprises five categories: (1) review metadata and temporal information, (2) raw review text, (3) book style descriptors, (4) narrative and thematic content, (5) emotional attachment indicators. With 76.0% of reviews mentioning humor, 65.9% expressing love, and 55.8% discussing cultural representation, this resource enables researchers to examine how stylistic perceptions relate to emotional attachment in cross-cultural contexts. Initial analysis reveals that readers describing the book as unique, whimsical, or innovative are significantly more likely to express emotional attachment, as are those drawn to illustrations and characters. By providing both structured data and transparent coding workflows, this dataset advances reproducible research practices in digital humanities and reader response studies while offering practical templates for similar datasets in emerging literary markets.