COMBINING TEXT CLASSIFICATION WITH MACHINE LEARNING

  • I. Nwade Department of Mechanical Engineering, University of North Dakota, USA
  • P. Ozoh Department of ICT, Osun State University, Nigeria
  • M. Olayiwola Department of Mathematical Sciences, Osun State University, Nigeria
  • M. Ibrahim Department of ICT, Osun State University, Nigeria
  • M. Kolawole Department of Mathematical Sciences, Osun State University, Nigeria
  • O. Olubusayo Department of Physics, Osun State University, Nigeria
  • A. Adigun Department of ICT, Osun State University, Nigeria
Keywords: Text classification, Internet Community, Random Forest (RF), Insight, Data scraping

Abstract

The internet community is a medium of conveying information for various individuals. Enterprises need to have an insight into their various products and services. For the time being, not automatically reading and classifying plenty of text data could be overwhelming. As a result, this research appropriates the Data scraping method in which datasets are collected via a code written in python programming. Machine learning models were evolved using Random Forest and Naïve Bayes algorithms which are trained with extracted data for text classification. Consequently, texts are classified as positive, negative, slightly negative, slightly positive, or neutral comments utilizing classifiers, in which the experimental outputs indicate that the Random Forest classifier produces more preferable results in terms of reliability with 76.5% above the Naïve Bayes with 70.01%.

Published
2023-07-08
How to Cite
Nwade, I., Ozoh, P., Olayiwola, M., Ibrahim, M., Kolawole, M., Olubusayo, O., & Adigun, A. (2023). COMBINING TEXT CLASSIFICATION WITH MACHINE LEARNING. LAUTECH Journal of Engineering and Technology, 17(2), 9-17. Retrieved from https://laujet.com/index.php/laujet/article/view/573