Text Mining: Current Trends and Applications

Text Mining: Current Trends and Applications

Kijpokin Kasemsap (Suan Sunandha Rajabhat University, Thailand)
DOI: 10.4018/978-1-5225-1877-8.ch017
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Abstract

This chapter reveals the overview of text mining; text mining, patent analysis, and keyword selection; text mining and sentiment analysis in modern marketing; text mining applications in the biomedical sciences; and the multifaceted applications of text mining. Text mining is an advanced technology utilized in business, marketing, biomedical sciences, education, and operations. Text mining offers a solution to many problems, drawing on techniques concerning information retrieval, natural language processing, information extraction, and knowledge management. Through text mining, information can be extracted to derive summaries for the words contained in the documents. Text mining has the potential to increase the research base available to business and society and to enable business to utilize the research base more effectively. Economic and societal benefits of text mining include cost savings, productivity gains, innovative new service development, new business models, and new medical treatments.
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Introduction

There is a tremendous growth in the volume of online text documents from networked resources, such as the Internet, digital libraries, and company-wide intranets (Kim, 2009). Many text mining approaches are based on words in the texts (Loh, Wives, Lichtnow, & de Oliveira, 2009). Text mining deals with how to extract the latent knowledge from the unstructured textual descriptions (Yoon, Park, & Coh, 2014). Text mining requires the highly scalable algorithms to meet the overall performance demands (Indurkhya, 2015) and facilitates the identification of relevant literature, its rapid categorization, and its summarization (Thomas, McNaught, & Ananiadou, 2011). Text mining has adopted certain techniques from the more general field of data analysis, including sophisticated methods for analyzing relationships among highly formatted data, such as numerical data or data with a relatively small fixed number of possible values (Kao et al., 2012).

Text mining can be broadly defined as a knowledge-intensive process in which a user interacts with a document collection over time by using a combination of analysis tools (Cheney, 2015). The good text knowledge representation model should contain rich text semantics and should automatically construct with a lower complexity (Zhang, Luo, He, & Cai, 2013). Text mining involves six main phases (i.e., business understanding, data understanding, data preparation, modeling, evaluation, and deployment). Perovšek et al. (2016) indicated that text mining can be distinguished from general data mining by special procedures applied in the data preparation phase, where unstructured or poorly structured text needs to be converted into organized data, structured as a table of instances (rows) described by attributes (columns).

This chapter aims to bridge the gap in the literature on the thorough literature consolidation of text mining. The extensive literature of text mining provides a contribution to practitioners and researchers by describing the trends and applications of text mining in order to maximize the technological impact of text mining in the digital age.

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