Survey on supervised machine learning techniques for automatic text classification

• Published in: The Artificial intelligence review Vol. 52; no. 1; pp. 273 - 292
• Main Author: Kadhim, Ammar Ismael
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.06.2019; Springer; Springer Nature B.V
• Subjects: Artificial Intelligence; Automatic text analysis; Classification; Computational linguistics; Computer Science; Data collection; Documents; Electronic documents; Information retrieval; Keywords; Language processing; Machine learning; Methods; Natural language interfaces; Natural language processing; Neural networks; Performance evaluation; Polls & surveys; Supervised learning; Text categorization; Text editing; Vector space; Weighting methods; Word sense disambiguation; Classification techniques; Supervised machine learning; Text classification; Term weighting
• ISSN: 0269-2821; 1573-7462
• DOI: 10.1007/s10462-018-09677-1

Supervised machine learning studies are gaining more significant recently because of the availability of the increasing number of the electronic documents from different resources. Text classification can be defined that the task was automatically categorized a group documents into one or more predefined classes according to their subjects. Thereby, the major objective of text classification is to enable users for extracting information from textual resource and deals with process such as retrieval, classification, and machine learning techniques together in order to classify different pattern. In text classification technique, term weighting methods design suitable weights to the specific terms to enhance the text classification performance. This paper surveys of text classification, process of different term weighing methods and comparison between different classification techniques.