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Analyzing the relationship between text features and grants productivity

Author

Listed:
  • Jorge A. V. Tohalino

    (University of São Paulo)

  • Laura V. C. Quispe

    (University of São Paulo)

  • Diego R. Amancio

    (University of São Paulo)

Abstract

Predicting the output of research grants is of considerable relevance to research funding bodies, scientific entities and government agencies. In this study, we investigate whether text features extracted from projects title and abstracts are able to identify productive grants. Our analysis was conducted in three distinct areas, namely Medicine, Dentistry and Veterinary Medicine. Topical and complexity text features were used to identify predictors of productivity. The results indicate that there is a statistically significant relationship between text features and grants productivity, however such a dependence is weak. A feature relevance analysis revealed that the abstract text length and metrics derived from lexical diversity are among the most discriminative features. We also found that the prediction accuracy has a dependence on the considered project language and that topical features are more discriminative than text complexity measurements. Our findings suggest that text features should be used in combination with other features to assist the identification of relevant research ideas.

Suggested Citation

  • Jorge A. V. Tohalino & Laura V. C. Quispe & Diego R. Amancio, 2021. "Analyzing the relationship between text features and grants productivity," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4255-4275, May.
    • Handle: RePEc:spr:scient:v:126:y:2021:i:5:d:10.1007_s11192-021-03926-x
    DOI: 10.1007/s11192-021-03926-x
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    Cited by:

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    3. Kumar, Dhananjay & Bhowmick, Plaban Kumar & Paik, Jiaul H, 2023. "Researcher influence prediction (ResIP) using academic genealogy network," Journal of Informetrics, Elsevier, vol. 17(2).
    4. Tohalino, Jorge A.V. & Amancio, Diego R., 2022. "On predicting research grants productivity via machine learning," Journal of Informetrics, Elsevier, vol. 16(2).

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