A brief survey of Text Mining and its applications

Abstract

The world came across the worst pandemic of all times in the year 2020 due to the outburst of Severe Acute Respiratory Syndrome Coronavirus-2 or Covid-19. All the questions about this outbreak were piled up and research was fast growing [1]. A study showing that in precisely just six months, substantial databases have been swamped with research articles, news, notes, and editorial related to coronavirus. It estimates that 23,634 distinctly published articles have been indexed on Web of Science and Scopus between 1 January and 30 June 2020. Imagine the data that is with us today!! Approximately 200,000 scholarly articles have been published related to Covid-19. This tells us that there is a need for simplifying search results to get answers to high priority questions for users specifically scientists. Currently, document clustering tools are being used in many areas. A similar clustering tool can be made particularly for Covid-19 which will help scientists and researchers get answers to high priority questions about this pandemic. In this paper, we are discussing about the process of text mining, text categorization and, text clustering. Also, a comparison of the algorithms used for clustering particularly in text data.

Performance Comparison of K-Means and DBScan Algorithms for Text Clustering Product Reviews

“…In doing text mining, structured data will make it easier and produce the appropriate output. The data source used in this study is unstructured data; therefore, this data needs to process first to produce cleaner, structured and ready data for analysis (Chandrayan & Bamne, 2021). In the preprocessing stage, there are several methods used to generate tokens that will used for the next stage, and it produced 57 list of words that will be using in the text clustering process.…”