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Semantic Academic Profiler (SAP): a framework for researcher assessment based on semantic topic modeling

Author

Listed:
  • Felipe Viegas

    (Federal University of Minas Gerais)

  • Antônio Pereira

    (Federal University of São João Del Rey)

  • Pablo Cecílio

    (Federal University of São João Del Rey)

  • Elisa Tuler

    (Federal University of São João Del Rey)

  • Wagner Meira

    (Federal University of Minas Gerais)

  • Marcos Gonçalves

    (Federal University of Minas Gerais)

  • Leonardo Rocha

    (Federal University of São João Del Rey)

Abstract

Recent efforts have focused on identifying multidisciplinary teams and detecting co-Authorship Networks based on exploring topic modeling to identify researchers’ expertise. Though promising, none of these efforts perform a real-life evaluation of the quality of the built topics. This paper proposes a Semantic Academic Profiler (SAP) framework that allows summarizing articles written by researchers to automatically build research profiles and perform online evaluations regarding these built profiles. SAP exploits and extends state-of-the-art Topic Modeling strategies based on Cluwords considering n-grams and introduces a new visual interface able to highlight the main topics related to articles, researchers and institutions. To evaluate SAP’s capability of summarizing the profile of such entities as well as its usefulness for supporting online assessments of the topics’ quality, we perform and contrast two types of evaluation, considering an extensive repository of Brazilian curricula vitae: (1) an offline evaluation, in which we exploit a traditional metric (NPMI) to measure the quality of several data representations strategies including (i) TFIDF, (ii) TFIDF with Bi-grams, (iii) Cluwords, and (iv) CluWords with Bi-grams; and (2) an online evaluation through an A/B test where researchers evaluate their own built profiles. We also perform an online assessment of SAP user interface through a usability test following the SUS methodology. Our experiments indicate that the CluWords with Bi-grams is the best solution and the SAP interface is very useful. We also observed essential differences in the online and offline assessments, indicating that using both together is very important for a comprehensive quality evaluation. Such type of study is scarce in the literature and our findings open space for new lines of investigation in the Topic Modeling area.

Suggested Citation

  • Felipe Viegas & Antônio Pereira & Pablo Cecílio & Elisa Tuler & Wagner Meira & Marcos Gonçalves & Leonardo Rocha, 2022. "Semantic Academic Profiler (SAP): a framework for researcher assessment based on semantic topic modeling," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(8), pages 5005-5026, August.
    • Handle: RePEc:spr:scient:v:127:y:2022:i:8:d:10.1007_s11192-022-04449-9
    DOI: 10.1007/s11192-022-04449-9
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    References listed on IDEAS

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    1. Daniel D. Lee & H. Sebastian Seung, 1999. "Learning the parts of objects by non-negative matrix factorization," Nature, Nature, vol. 401(6755), pages 788-791, October.
    2. Michael Gusenbauer, 2019. "Google Scholar to overshadow them all? Comparing the sizes of 12 academic search engines and bibliographic databases," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 177-214, January.
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    Cited by:

    1. Yajun Ji & Shengtai Zhang & Fang Han & Ran Cui & Tao Jiang, 2024. "The Sustainable Innovation of AI: Text Mining the Core Capabilities of Researchers in the Digital Age of Industry 4.0," Sustainability, MDPI, vol. 16(17), pages 1-17, September.
    2. Edré Moreira & Wagner Meira & Marcos André Gonçalves & Alberto H. F. Laender, 2023. "The rise of hyperprolific authors in computer science: characterization and implications," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(5), pages 2945-2974, May.

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