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Measuring book impact based on the multi-granularity online review mining

Author

Listed:
  • Qingqing Zhou

    (Nanjing University of Science and Technology
    Hangzhou Normal University)

  • Chengzhi Zhang

    (Nanjing University of Science and Technology
    Nanjing University
    Hangzhou Normal University)

  • Star X. Zhao

    (East China Normal University)

  • Bikun Chen

    (Nanjing University of Science and Technology)

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 identify review sentiment polarities and aspects’ sentiment values. Lastly, the numbers of positive reviews and negative reviews, aspect sentiment values, star values, and information regarding helpfulness are integrated via the entropy method, and lead to the calculation of the final book impact scores. The results of a correlation analysis of book impact scores obtained via our method versus traditional book citations show that, although there are substantial differences between subject areas, online book reviews tend to reflect the academic impact. Thus, we infer that online reviews represent a promising source for mining book impact within the altmetric perspective and at the multi-granularity content level. Moreover, our proposed method might also be a means by which to measure other books besides academic publications.

Suggested Citation

  • Qingqing Zhou & Chengzhi Zhang & Star X. Zhao & Bikun Chen, 2016. "Measuring book impact based on the multi-granularity online review mining," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(3), pages 1435-1455, June.
    • Handle: RePEc:spr:scient:v:107:y:2016:i:3:d:10.1007_s11192-016-1930-5
    DOI: 10.1007/s11192-016-1930-5
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    12. Alesia Zuccala & Maarten Someren & Maurits Bellen, 2014. "A machine-learning approach to coding book reviews as quality indicators: Toward a theory of megacitation," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(11), pages 2248-2260, November.
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    2. Mohammadamin Erfanmanesh & A. Noorhidawati & A. Abrizah, 2019. "What can Bookmetrix tell us about the impact of Springer Nature’s books," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 521-536, October.
    3. Siluo Yang & Xin Xing & Fan Qi & Maria Cláudia Cabrini Grácio, 2021. "Comparison of academic book impact from a disciplinary perspective: an analysis of citations and altmetric indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1101-1123, February.
    4. Maja Jokić & Andrea Mervar & Stjepan Mateljan, 2019. "Comparative analysis of book citations in social science journals by Central and Eastern European authors," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(3), pages 1005-1029, September.
    5. Qingqing Zhou & Chengzhi Zhang, 2020. "Evaluating wider impacts of books via fine-grained mining on citation literatures," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 1923-1948, December.
    6. Wang, Kai & Liu, Xiaojuan & Han, Yutong, 2019. "Exploring Goodreads reviews for book impact assessment," Journal of Informetrics, Elsevier, vol. 13(3), pages 874-886.
    7. Zhang, Chengzhi & Zhou, Qingqing, 2020. "Assessing books’ depth and breadth via multi-level mining on tables of contents," Journal of Informetrics, Elsevier, vol. 14(2).
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    9. Yajie Hu & Huiwen Zhou & Yuangao Chen & Jianrong Yao & Jiangwu Su, 2023. "The influence of patient-generated reviews and doctor-patient relationship on online consultations in China," Electronic Commerce Research, Springer, vol. 23(2), pages 1115-1141, June.

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