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Open AccessArticle

Text Mining in Big Data Analytics

1
Research Institute of Energy Management and Planning, University of Tehran, Tehran 1417466191, Iran
2
Department of Tourism, Faculty of Economic Sciences, Ionian University, Kalypso Building, 4 P. Vraila Armeni, 49100 Corfu, Greece
3
Department of Economics and Business, Saint Anselm College, 100 Saint Anselm Drive, Manchester, NH 03103, USA
4
Department of Management, University of Tehran, Tehran 1417466191, Iran
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Department of Accounting, Islamic Azad University, Central Tehran Branch, Tehran 1955847781, Iran
*
Author to whom correspondence should be addressed.
Big Data Cogn. Comput. 2020, 4(1), 1; https://doi.org/10.3390/bdcc4010001
Received: 18 November 2019 / Revised: 11 January 2020 / Accepted: 11 January 2020 / Published: 16 January 2020
(This article belongs to the Special Issue Knowledge Modelling and Learning through Cognitive Networks)
Text mining in big data analytics is emerging as a powerful tool for harnessing the power of unstructured textual data by analyzing it to extract new knowledge and to identify significant patterns and correlations hidden in the data. This study seeks to determine the state of text mining research by examining the developments within published literature over past years and provide valuable insights for practitioners and researchers on the predominant trends, methods, and applications of text mining research. In accordance with this, more than 200 academic journal articles on the subject are included and discussed in this review; the state-of-the-art text mining approaches and techniques used for analyzing transcripts and speeches, meeting transcripts, and academic journal articles, as well as websites, emails, blogs, and social media platforms, across a broad range of application areas are also investigated. Additionally, the benefits and challenges related to text mining are also briefly outlined. View Full-Text
Keywords: text mining; big data; analytics; review text mining; big data; analytics; review
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MDPI and ACS Style

Hassani, H.; Beneki, C.; Unger, S.; Mazinani, M.T.; Yeganegi, M.R. Text Mining in Big Data Analytics. Big Data Cogn. Comput. 2020, 4, 1. https://doi.org/10.3390/bdcc4010001

AMA Style

Hassani H, Beneki C, Unger S, Mazinani MT, Yeganegi MR. Text Mining in Big Data Analytics. Big Data and Cognitive Computing. 2020; 4(1):1. https://doi.org/10.3390/bdcc4010001

Chicago/Turabian Style

Hassani, Hossein; Beneki, Christina; Unger, Stephan; Mazinani, Maedeh T.; Yeganegi, Mohammad R. 2020. "Text Mining in Big Data Analytics" Big Data Cogn. Comput. 4, no. 1: 1. https://doi.org/10.3390/bdcc4010001

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