A Survey on Text Mining - Techniques, Application

Abstract

Text mining, also known as text data mining or text analytics, is a field of study that focuses on extracting meaningful information and knowledge from textual data. The rapid advancement of digital data acquisition techniques has resulted in an unprecedented volume of data. In fact, over 80 percent of the data generated today comprises unstructured or semi-structured formats. Extracting meaningful patterns and trends from such massive amounts of text data poses a significant challenge. Text mining addresses this challenge by extracting valuable and nontrivial patterns from vast collections of text documents. Various techniques and tools are available for mining text documents and uncovering valuable information to inform decision-making and future processing. Selecting the appropriate text mining technique is crucial as it can significantly enhance the speed and efficiency of retrieving valuable information, reducing the time and effort required. This paper provides a concise analysis and discussion of text mining techniques and their applications. As technology continues to advance, the availability of digital data continues to increase. A substantial portion, approximately 85 percent, of this data exists in unstructured textual form. Consequently, it has become imperative to develop improved techniques and algorithms to effectively extract useful and interesting information from these vast amounts of textual data. This has resulted in the emergence of information extraction and text mining as popular research areas dedicated to uncovering valuable and necessary information from textual data.