A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques

TL;DR

This survey reviews fundamental text mining techniques such as classification, clustering, and extraction, highlighting their applications in biomedical and healthcare domains amidst increasing unstructured text data.

Contribution

It provides a comprehensive overview of core text mining tasks and techniques, including recent applications in biomedical and health care fields.

Findings

Summarizes key text mining methods and algorithms.

Highlights applications in biomedical and healthcare domains.

Emphasizes importance of efficient processing of unstructured text.

Abstract

The amount of text that is generated every day is increasing dramatically. This tremendous volume of mostly unstructured text cannot be simply processed and perceived by computers. Therefore, efficient and effective techniques and algorithms are required to discover useful patterns. Text mining is the task of extracting meaningful information from text, which has gained significant attentions in recent years. In this paper, we describe several of the most fundamental text mining tasks and techniques including text pre-processing, classification and clustering. Additionally, we briefly explain text mining in biomedical and health care domains.