Fine-grained classification of social science journal articles using textual data: A comparison of supervised machine learning approaches
AbstractWe compare two supervised machine learning algorithms—Multinomial Naïve Bayes and Gradient Boosting—to classify social science articles using textual data. The high level of granularity of the classification scheme used and the possibility that multiple categories are assigne...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
The MIT Press
2021-01-01
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| Series: | Quantitative Science Studies |
| Online Access: | https://direct.mit.edu/qss/article/2/1/89/97077/Fine-grained-classification-of-social-science |