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Interpreting maps of science using citation context sentiments: a preliminary investigation

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  • Henry Small

    (Thomson Reuters
    SciTech Strategies, Inc)

Abstract

It is proposed that citation contexts, the text surrounding references in scientific papers, be analyzed in terms of an expanded notion of sentiment, defined to include attitudes and dispositions toward the cited work. Maps of science at both the specialty and global levels are used as the basis of this analysis. Citation context samples are taken at these levels and contrasted for the appearance of cue word sets, analyzed with the aid of methods from corpus linguistics. Sentiments are shown to vary within a specialty and can be understood in terms of cognitive and social factors. Within-specialty and between-specialty co-citations are contrasted and in some cases suggest a correlation of sentiment with structural location. For example, the sentiment of “uncertainty” is important in interdisciplinary co-citation links, while “utility” is more prevalent within the specialty. Suggestions are made for linking sentiments to technical terms, and for developing sentiment “baselines” for all of science.

Suggested Citation

  • Henry Small, 2011. "Interpreting maps of science using citation context sentiments: a preliminary investigation," Scientometrics, Springer;Akadémiai Kiadó, vol. 87(2), pages 373-388, May.
    • Handle: RePEc:spr:scient:v:87:y:2011:i:2:d:10.1007_s11192-011-0349-2
    DOI: 10.1007/s11192-011-0349-2
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    References listed on IDEAS

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    Cited by:

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    5. Chaker Jebari & Enrique Herrera-Viedma & Manuel Jesus Cobo, 2023. "Context-aware citation recommendation of scientific papers: comparative study, gaps and trends," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(8), pages 4243-4268, August.
    6. Chaker Jebari & Enrique Herrera-Viedma & Manuel Jesus Cobo, 2021. "The use of citation context to detect the evolution of research topics: a large-scale analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 2971-2989, April.
    7. Rey-Long Liu, 2017. "A new bibliographic coupling measure with descriptive capability," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(2), pages 915-935, February.
    8. Maryam Yaghtin & Hajar Sotudeh & Mahdieh Mirzabeigi & Seyed Mostafa Fakhrahmad & Mehdi Mohammadi, 2019. "In quest of new document relations: evaluating co-opinion relations between co-citations and its impact on Information retrieval effectiveness," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(2), pages 987-1008, May.
    9. Kai Li & Jason Rollins & Erjia Yan, 2018. "Web of Science use in published research and review papers 1997–2017: a selective, dynamic, cross-domain, content-based analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 1-20, April.
    10. Imran Ihsan & M. Abdul Qadir, 2021. "An NLP-based citation reason analysis using CCRO," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(6), pages 4769-4791, June.
    11. Hu, Zhigang & Chen, Chaomei & Liu, Zeyuan, 2013. "Where are citations located in the body of scientific articles? A study of the distributions of citation locations," Journal of Informetrics, Elsevier, vol. 7(4), pages 887-896.
    12. Shengzhi Huang & Jiajia Qian & Yong Huang & Wei Lu & Yi Bu & Jinqing Yang & Qikai Cheng, 2022. "Disclosing the relationship between citation structure and future impact of a publication," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(7), pages 1025-1042, July.
    13. Rey-Long Liu, 2015. "Passage-Based Bibliographic Coupling: An Inter-Article Similarity Measure for Biomedical Articles," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-22, October.
    14. Mingyang Wang & Jiaqi Zhang & Shijia Jiao & Xiangrong Zhang & Na Zhu & Guangsheng Chen, 2020. "Important citation identification by exploiting the syntactic and contextual information of citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2109-2129, December.
    15. Bikun Chen & Dannan Deng & Zhouyan Zhong & Chengzhi Zhang, 2020. "Exploring linguistic characteristics of highly browsed and downloaded academic articles," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(3), pages 1769-1790, March.
    16. Sergey Parinov & Mikhail Kogalovsky, 2014. "Semantic linkages in research information systems as a new data source for scientometric studies," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(2), pages 927-943, February.
    17. Tomaz Bartol & Karmen Stopar, 2015. "Nano language and distribution of article title terms according to power laws," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(2), pages 435-451, May.
    18. Jeong, Yoo Kyung & Song, Min & Ding, Ying, 2014. "Content-based author co-citation analysis," Journal of Informetrics, Elsevier, vol. 8(1), pages 197-211.
    19. Wang, Shiyun & Mao, Jin & Lu, Kun & Cao, Yujie & Li, Gang, 2021. "Understanding interdisciplinary knowledge integration through citance analysis: A case study on eHealth," Journal of Informetrics, Elsevier, vol. 15(4).
    20. Min Song & Keun Young Kang & Tatsawan Timakum & Xinyuan Zhang, 2020. "Examining influential factors for acknowledgements classification using supervised learning," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-21, February.
    21. Michel Zitt, 2015. "Meso-level retrieval: IR-bibliometrics interplay and hybrid citation-words methods in scientific fields delineation," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 2223-2245, March.

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