Manual Analysis of massive amounts of textual data necessitates a tremendous amount of processing time and effort in interpreting the text and organizing it in the required format. Because of the high dimensionality of feature space, the main issue in the current scenario is text or document categorization. There are numerous methods available today for dealing with text feature selection. This paper aims to present one such semi-automated text categorization feature selection methodology for dealing with massive data sets by employing two phases of David Merrill’s First Principles of Instruction (FPI). It employs a pre-defined category group by providing the appropriate training set based on the FPI’s demonstration and integration phases Text tokenization, text categorization, and text analysis are all part of the methodology.
Author (S) Details
Dr. (Mrs.) M. Pushpa
Department of Computer Science, Quiad-e-Millath Government Arts College for Women, Chennai, India.
Dr. (Mrs.) K. Nirmala
Department of Computer Science, Quiad-e-Millath Government Arts College for Women, Chennai, India.
Dr. (Mrs.) J. Vijayalakshmi
Department of Information Technology, Sri Sai Ram Engineering College, West Tambaram, Chennai, India.
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