Urdu Text Classification using Majority Voting

Abstract

Text classification is a tool to assign the predefined categories to the text documents using supervised machine learning algorithms. It has various practical applications like spam detection, sentiment detection, and detection of a natural language. Based on the idea we applied five well-known classification techniques on Urdu language corpus and assigned a class to the documents using majority voting. The corpus contains 21769 news documents of seven categories (Business, Entertainment, Culture, Health, Sports, and Weird). The algorithms were not able to work directly on the data, so we applied the preprocessing techniques like tokenization, stop words removal and a rule-based stemmer. After preprocessing 93400 features are extracted from the data to apply machine learning algorithms. Furthermore, we achieved up to 94% precision and recall using majority voting.