The current study employs machine learning techniques to predict subjects for studies. We derive models that efficiently classify the papers into predefined subject areas by using textual data extraction from research papers.
Methodology:
- Organize a collection of research papers including text data and related subjects.
- Cleaning the text data—including tokenizing, stop-word elimination, and stemming.
- Feature Extraction: Transforms text data into numerical values by TF-IDF among other
- Including support vector machines, logistic regression, and naive bayes, models are
- Value model performance by means of precision, precision, accuracy, recall, and F1-
Predicting research topics, the Support Vector Machine model produces the highest degree of precision. Academic researchers may gain much from this study of machine learning in text classification to effectively classify papers for research.