Measuring Book Impact Based on the Multi-granularity Online Review Mining
Qingqing Zhou, Chengzhi Zhang, Star X. Zhao, Bikun Chen

TL;DR
This paper proposes a novel method to measure academic book impact by analyzing online reviews at multiple levels, integrating sentiment and helpfulness data, and correlating results with traditional citation metrics.
Contribution
It introduces a multi-granularity review mining approach that combines sentiment analysis and entropy-based scoring to assess book impact from online reviews, expanding impact measurement beyond citations.
Findings
Online reviews correlate with traditional citation-based impact measures.
The method effectively captures content-level impact factors.
Online reviews can serve as a promising alternative for impact assessment.
Abstract
As with articles and journals, the customary methods for measuring books' academic impact mainly involve citations, which is easy but limited to interrogating traditional citation databases and scholarly book reviews, Researchers have attempted to use other metrics, such as Google Books, libcitation, and publisher prestige. However, these approaches lack content-level information and cannot determine the citation intentions of users. Meanwhile, the abundant online review resources concerning academic books can be used to mine deeper information and content utilizing altmetric perspectives. In this study, we measure the impacts of academic books by multi-granularity mining online reviews, and we identify factors that affect a book's impact. First, online reviews of a sample of academic books on Amazon.cn are crawled and processed. Then, multi-granularity review mining is conducted to…
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Taxonomy
TopicsSentiment Analysis and Opinion Mining · Advanced Text Analysis Techniques · Digital Marketing and Social Media
