Abstract :
Classification of data has become an important research area. The process of classifying documents into predefined categories based on their content is Text classification. It is the automated assignment of natural language texts to predefined categories. The primary requirement of text retrieval systems is text classification, which retrieve texts in response to a user query, and text understanding systems, which transform text in some way such as answering questions, producing summaries or extracting data. In this paper we are studying the various classification algorithms. Classification is the process of dividing the data to some groups that can act either dependently or independently. Our main aim is to show the comparison of the various classification algorithms like K nn, Naïve Bayes, Decision Tree, Random Forest and Support Vector Machine SVM with rapid miner and find out which algorithm will be most suitable for the users.
by Nida Zafar Khan | Prof. S. R. Yadav "Analysis of Text Classification Algorithms: A Review"
Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-2 , February 2019,
URL: https://www.ijtsrd.com/papers/ijtsrd21448.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/21448/analysis-of-text-classification-algorithms-a-review/nida-zafar-khan
Keyword :
Text Mining, K-nn, Naïve Bayes, Decision Tree, Random Forest and Support Vector Machine, Rapid miner