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All Journal International Journal of Electrical and Computer Engineering
Hamou Aadi, Fatima Zahrae Ait
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Computer Science & IT Electrical & Electronics Engineering

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Text classification supervised algorithms with term frequency inverse document frequency and global vectors for word representation: a comparative study Labd, Zakia; Bahassine, Said; Housni, Khalid; Hamou Aadi, Fatima Zahrae Ait; Benabbes, Khalid
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp589-599

Abstract

Over the course of the previous two decades, there has been a rise in the quantity of text documents stored digitally. The ability to organize and categorize those documents in an automated mechanism, is known as text categorization which is used to classify them into a set of predefined categories so they may be preserved and sorted more efficiently. Identifying appropriate structures, architectures, and methods for text classification presents a challenge for researchers. This is due to the significant impact this concept has on content management, contextual search, opinion mining, product review analysis, spam filtering, and text sentiment mining. This study analyzes the generic categorization strategy and examines supervised machine learning approaches and their ability to comprehend complex models and nonlinear data interactions. Among these methods are k-nearest neighbors (KNN), support vector machine (SVM), and ensemble learning algorithms employing various evaluation techniques. Thereafter, an evaluation is conducted on the constraints of every technique and how they can be applied to real-life situations.
Co-Authors Bahassine, Said Benabbes, Khalid Housni, Khalid Labd, Zakia