Document Categorization Using Decision Tree: Preliminary Study

Abstract

This preliminaries study aims to propose a good classification technique that capable of doing document classification based on text mining technique and create an algorithm to automatically classify document according to its folder based on document’s content while able to do sentiment analyses to data sets and summarize it. The objective of this paper to identify an efficient text mining classification technique which can resulted with highest accuracy of classifying document into document folder, capable of extracting valuable information from context-based term that can be used as an output for algorithm to do automatic classification and evaluate the classification technique. Methodology of this study comprises in 5 modules which is 1) Document collection, 2) Pre-Processing Stage, 3) Term Frequency-Inversed Document Frequency, 4) Classification Technique and Algorithm, and lastly 5) Evaluation and Visualization of the classification result. The proposed framework will have utilized Term Frequency-Inversed Document Frequency (TF-IDF) and Decision Tree technique which TF-IDF used as purposes to rank all the terms based on most frequent to least frequent terms so, while decision tree function as decision making in terms of deciding which folder the document belongs to.

Textual Dataset Classification Using Supervised Machine Learning Techniques

“…The results verify that the proposed technique can significantly enhance classification performance. Noor man shah et al [12] provided a document categorization using the decision tree technique that outcomes in a high accuracy value of classifying text documents to their classes. The proposed approach combined the TF-IDF with a decision tree, where TF-IDF is utilized to assort all words from most repeated to less repeated words.…”

“…The decision tree algorithm is iterated on every divided data pa rtition, producing subtrees till the training data is subdivided into subsets of similar classes. At each stage of the partitioning ta sk, a statistical criterion known as Information Gain (IG) is used to decide what features best split the training text records [3,12]. The DT classifier implies two phases:…”

“…In constructing Decision Trees, the objective is to decrease the dataset entropy till it reaches leaf nodes, such that the subset holds zero Entropy and represents all examples of one class, i.e., all data examples involve the same value of the target feature or class value. To compute the Entropy of the data set S, the following formula can be utilized [12]:…”

A Review on Non-Supervised Approaches for Cyberbullying Detection

“…The writers in [17] categorized messages into clean and abusive. However, the writers in [18] used another technique for clustering, which is the Apriori Algorithm to analyze abusive tweets but in Malay language.…”

Relationship Analysis on the Experience of Hospitalised Paediatric Cancer Patient in Malaysia using Text Analytics Approach

“…According to [27], Text Mining (TM) can be defined as an analytic process that is designed to explore the unstructured text documents in search of useful information and knowledge hidden from a large amount of text resources. TM has been widely applied in many application domains such as military knowledge [28][29][30][31], business analytics [32][33][34], documents analysis [35][36][37], social media issues [38][39][40], etc. In this research, TM which is one of DM method is explored as to find new knowledge through examining qualitative data of patient experiences while having chemo treatment [20,21].…”