IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v119y2019i2d10.1007_s11192-019-03058-3.html
   My bibliography  Save this article

In quest of new document relations: evaluating co-opinion relations between co-citations and its impact on Information retrieval effectiveness

Author

Listed:
  • Maryam Yaghtin

    (Shiraz University)

  • Hajar Sotudeh

    (Shiraz University)

  • Mahdieh Mirzabeigi

    (Shiraz University)

  • Seyed Mostafa Fakhrahmad

    (Shiraz University)

  • Mehdi Mohammadi

    (Shiraz University)

Abstract

Document relational network has been effective in retrieving and evaluating papers. Despite their effectiveness, relational measures, including co-citation, are far from ideal and need improvements. The assumption underlying the co-citation relation is the content relevance and opinion relatedness of cited and citing papers. This may imply existence of some kind of co-opinionatedness between co-cited papers which may be effective in improving the measure. Therefore, the present study tries to test the existence of this phenomenon and its role in improving information retrieval. To do so, based on CITREC, a medical test collection was developed consisting of 30 queries (seed documents) and 4823 of their co-cited papers. Using NLP techniques, the co-citances of the queries and their co-cited papers were analyzed and their similarities were computed by 4 g similarity measure. Opinion scores were extracted from co-citances using SentiWordnet. Also, nDCG values were calculated and then compared in terms of the citation proximity index (CPI) and co-citedness measures before and after being normalized by the co-opinionatedness measure. The reliability of the test collection was measured by generalizability theory. The findings suggested that a majority of the co-citations exhibited a high level of co-opinionatedness in that they were mostly similar either in their opinion strengths or in their polarities. Although anti-polar co-citations were not trivial in their number, a significantly higher number of the co-citations were co-polar, with a majority being positive. The evaluation of the normalization of the CPI and co-citedness by the co-opinionatedness indicated a generally significant improvement in retrieval effectiveness. While anti-polar similarity reduced the effectiveness of the measure, the co-polar similarity proved to be effective in improving the co-citedness. Consequently, the co-opinionatedness can be presented as a new document relation and used as a normalization factor to improve retrieval performance and research evaluation.

Suggested Citation

  • 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.
  • Handle: RePEc:spr:scient:v:119:y:2019:i:2:d:10.1007_s11192-019-03058-3
    DOI: 10.1007/s11192-019-03058-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-019-03058-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11192-019-03058-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Shengbo Liu & Chaomei Chen & Kun Ding & Bo Wang & Kan Xu & Yuan Lin, 2014. "Literature retrieval based on citation context," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1293-1307, November.
    2. Terrence A. Brooks, 1985. "Private acts and public objects: An investigation of citer motivations," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 36(4), pages 223-229, July.
    3. Ben‐Ami Lipetz, 1965. "Improvement of the selectivity of citation indexes to science literature through inclusion of citation relationship indicators," American Documentation, Wiley Blackwell, vol. 16(2), pages 81-90, April.
    4. Metin Doslu & Haluk O. Bingol, 2016. "Context sensitive article ranking with citation context analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(2), pages 653-671, August.
    5. Alison Callahan & Stephen Hockema & Gunther Eysenbach, 2010. "Contextual cocitation: Augmenting cocitation analysis and its applications," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(6), pages 1130-1143, June.
    6. Ying Ding & Guo Zhang & Tamy Chambers & Min Song & Xiaolong Wang & Chengxiang Zhai, 2014. "Content-based citation analysis: The next generation of citation analysis," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(9), pages 1820-1833, September.
    7. Kevin W. Boyack & Henry Small & Richard Klavans, 2013. "Improving the accuracy of co-citation clustering using full text," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(9), pages 1759-1767, September.
    8. Michael H. MacRoberts & Barbara R. MacRoberts, 1989. "Problems of citation analysis: A critical review," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 40(5), pages 342-349, September.
    9. Henry Small, 1973. "Co‐citation in the scientific literature: A new measure of the relationship between two documents," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 24(4), pages 265-269, July.
    10. Julie Bichteler & Edward A. Eaton, 1980. "The combined use of bibliographic coupling and cocitation for document retrieval," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 31(4), pages 278-282, August.
    11. Jeong, Yoo Kyung & Song, Min & Ding, Ying, 2014. "Content-based author co-citation analysis," Journal of Informetrics, Elsevier, vol. 8(1), pages 197-211.
    12. 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.
    13. Aaron Elkiss & Siwei Shen & Anthony Fader & Güneş Erkan & David States & Dragomir Radev, 2008. "Blind men and elephants: What do citation summaries tell us about a research article?," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 59(1), pages 51-62, January.
    14. Masaki Eto, 2013. "Evaluations of context-based co-citation searching," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(2), pages 651-673, February.
    15. Alison Callahan & Stephen Hockema & Gunther Eysenbach, 2010. "Contextual cocitation: Augmenting cocitation analysis and its applications," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(6), pages 1130-1143, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kamal Sanguri & Atanu Bhuyan & Sabyasachi Patra, 2020. "A semantic similarity adjusted document co-citation analysis: a case of tourism supply chain," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 233-269, October.
    2. Elisa Baraibar-Diez & Manuel Luna & María D. Odriozola & Ignacio Llorente, 2020. "Mapping Social Impact: A Bibliometric Analysis," Sustainability, MDPI, vol. 12(22), pages 1-20, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kim, Ha Jin & Jeong, Yoo Kyung & Song, Min, 2016. "Content- and proximity-based author co-citation analysis using citation sentences," Journal of Informetrics, Elsevier, vol. 10(4), pages 954-966.
    2. Masaki Eto, 2013. "Evaluations of context-based co-citation searching," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(2), pages 651-673, February.
    3. Dangzhi Zhao & Andreas Strotmann, 2020. "Telescopic and panoramic views of library and information science research 2011–2018: a comparison of four weighting schemes for author co-citation analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 255-270, July.
    4. Marc Bertin & Iana Atanassova & Cassidy R. Sugimoto & Vincent Lariviere, 2016. "The linguistic patterns and rhetorical structure of citation context: an approach using n-grams," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(3), pages 1417-1434, December.
    5. Raja Habib & Muhammad Tanvir Afzal, 2019. "Sections-based bibliographic coupling for research paper recommendation," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(2), pages 643-656, May.
    6. Jeong, Yoo Kyung & Song, Min & Ding, Ying, 2014. "Content-based author co-citation analysis," Journal of Informetrics, Elsevier, vol. 8(1), pages 197-211.
    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. 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.
    9. Sehrish Iqbal & Saeed-Ul Hassan & Naif Radi Aljohani & Salem Alelyani & Raheel Nawaz & Lutz Bornmann, 2021. "A decade of in-text citation analysis based on natural language processing and machine learning techniques: an overview of empirical studies," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6551-6599, August.
    10. Shengbo Liu & Chaomei Chen & Kun Ding & Bo Wang & Kan Xu & Yuan Lin, 2014. "Literature retrieval based on citation context," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1293-1307, November.
    11. Kamal Sanguri & Atanu Bhuyan & Sabyasachi Patra, 2020. "A semantic similarity adjusted document co-citation analysis: a case of tourism supply chain," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 233-269, October.
    12. Annarelli, Alessandro & Battistella, Cinzia & Nonino, Fabio & Parida, Vinit & Pessot, Elena, 2021. "Literature review on digitalization capabilities: Co-citation analysis of antecedents, conceptualization and consequences," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    13. Chao Lu & Ying Ding & Chengzhi Zhang, 2017. "Understanding the impact change of a highly cited article: a content-based citation analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(2), pages 927-945, August.
    14. Shengbo Liu & Chaomei Chen, 2012. "The proximity of co-citation," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(2), pages 495-511, May.
    15. 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.
    16. 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).
    17. Jianhua Hou, 2017. "Exploration into the evolution and historical roots of citation analysis by referenced publication year spectroscopy," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(3), pages 1437-1452, March.
    18. Dongqing Lyu & Xuanmin Ruan & Juan Xie & Ying Cheng, 2021. "The classification of citing motivations: a meta-synthesis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3243-3264, April.
    19. 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.
    20. Tahamtan, Iman & Bornmann, Lutz, 2018. "Core elements in the process of citing publications: Conceptual overview of the literature," Journal of Informetrics, Elsevier, vol. 12(1), pages 203-216.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:scient:v:119:y:2019:i:2:d:10.1007_s11192-019-03058-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.