International Journal of Engineering Research

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Educational Document Classification Using Natural Language Processing


Affiliations
1 Department of CSE, K.S Institute of Technology, Bengaluru, India
 

The paper uses supervised machine learning and content-based document classification of textual documents that are confined to four educational departments-Civil, Computer Science, Mechanical and Electrical Engineering by using TF-IDF algorithm along with Natural Language Processing for feature selection and ID3 algorithm as a classifier. The results show 80% accuracy.

Keywords

Supervised Machine Learning, Natural Language Processing, TF-IDF, Iterative Dichotomiser 3.
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  • Educational Document Classification Using Natural Language Processing

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Authors

M. Spoorthi
Department of CSE, K.S Institute of Technology, Bengaluru, India
K. Srilekha
Department of CSE, K.S Institute of Technology, Bengaluru, India
J. Sanjana
Department of CSE, K.S Institute of Technology, Bengaluru, India
B. N. Kushal Kumar
Department of CSE, K.S Institute of Technology, Bengaluru, India

Abstract


The paper uses supervised machine learning and content-based document classification of textual documents that are confined to four educational departments-Civil, Computer Science, Mechanical and Electrical Engineering by using TF-IDF algorithm along with Natural Language Processing for feature selection and ID3 algorithm as a classifier. The results show 80% accuracy.

Keywords


Supervised Machine Learning, Natural Language Processing, TF-IDF, Iterative Dichotomiser 3.