Text Mining Technique for Driving Potentially Valuable Information from Text

Fantaye Ayele

doi:10.7176/ikm/10-1-01

Abstract

With the growing number of digitized documents and having large text databases, text mining will become increasingly important. Text mining can be a huge benefit for finding relevant and desired text data from unstructured data sources. Text Mining is the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources. It is an important step of Knowledge Discovery process. The aim of the paper is to study the concept of Text Mining and various techniques with a particular focus on text mining process. In the text mining community have been trying to apply many methods such as rule-based, knowledge based, statistical and machine-learning-based approaches. Finally, the paper discusses issues towards the techniques for driving potentially valuable information from text and also, discuss on integration data mining. The paper ends with conclusion and the future line of works in the combining text mining and data mining techniques into a single system, a combination known as duo-mining, and also be more effective text mining techniques for contextual extraction. Keywords: Data mining, Information Extraction, Information Retrieval, Text Mining DOI : 10.7176/IKM/10-1-01 Publication date: January 31 st 2020

Highlights

Text mining is a burgeoning technology that is still, because of its newness and intrinsic difficulty, in a fluid state-akin, perhaps, to the state of machine learning in the mid- 1980s
This increased amount of available textual information has led to a research field devoted to knowledge discovery in unstructured data known as text mining
Text mining techniques are incorporated for the applications like Information retrieval, Information Extraction, Summarization and Topic Discovery for necessary knowledge discovery process

Summary

Introduction

Text mining is a burgeoning technology that is still, because of its newness and intrinsic difficulty, in a fluid state-akin, perhaps, to the state of machine learning in the mid- 1980s. 3.2 Text mining process Text Mining starts with a collection of documents; which would retrieve a particular document and preprocess it by checking format and character sets It would go through a text analysis phase, sometimes repeating techniques until information is extracted. Text mining techniques are incorporated for the applications like Information retrieval, Information Extraction, Summarization and Topic Discovery for necessary knowledge discovery process. Some of the better text mining tools let users select particular categories of interest or the software automatically can even infer the user’s interests based on his/her reading history and click-through information It could be used in the medical industry by doctors and other people looking for new treatments for illnesses and who wish to keep up on the latest advancements (Gupta and Lehal, 2009). The first step in text clustering is to transform documents, which typically are strings of characters into a suitable representation for the clustering task

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Conclusion