Techniques, Methods, and Analysis of Text Mining: A Review

Abstract

Recent technological advances have led to the availability of new types of observations and measurements that were previously not available and that have fueled the ‘Big Data’ trend. Along with standard structured forms of data (containing mainly numbers), modern databases include new forms of unstructured data comprising words, images, sounds, and videos which require new techniques to be exploited and interpreted. This study focuses on Text Mining, which is a set of statistical and computer science techniques specifically developed to analyze text data. New sources of text data are now available, such as text messaging, social media activity, blogs, and web searches. The increasing availability of published text, sophisticated technologies, and growing interest in organizations in extracting information from text have led to replacing (or at least supplementing) the human effort with automatic systems. Text mining can be used for a variety of scopes, ranging from basic descriptions of text content through word counts to more sophisticated uses such as finding links between authors and evaluating the content of scripts (e.g., automated marking of essays). Its basic purpose is to process the unstructured information contained in text data in order to make text accessible to various Data mining statistical algorithms. This could help make text data as informative as standard structured data and allow us to investigate relationships and patterns that would otherwise be extremely difficult, if not impossible, to discover. This study takes a quick look at how to organize and analyze unstructured text data using R programming language. And implementing various text mining operations to clean and structure the “eng_news_2020” dataset. This study also represents some association between the words using the chi-square test and clustering procedure.