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Predicting the output of research grants is of considerable relevance toresearch funding bodies, scientific entities and government agencies. In thisstudy, we investigate whether text features extracted from projects title andabstracts are able to identify productive grants. Our analysis was conducted inthree distinct areas, namely Medicine, Dentistry and Veterinary Medicine.Topical and complexity text features were used to identify predictors ofproductivity. The results indicate that there is a statistically significantrelationship between text features and grants productivity, however such adependence is weak. A feature relevance analysis revealed that the abstracttext length and metrics derived from lexical diversity are among the mostdiscriminative features. We also found that the prediction accuracy has adependence on the considered project language and that topical features aremore discriminative than text complexity measurements. Our findings suggestthat text features should be used in combination with other features to assistthe identification of relevant research ideas.

Analyzing the relationship between text features and research proposal productivity