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Text data mining: a proposed framework and future perspectives

Author

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  • Sana'a A. Alwidian
  • Hani A. Bani-Salameh
  • Ala'a N. Alslaity

Abstract

With the increased advancements in technology and the emergence of different kinds of applications, the amount of available data becomes enormous, and the large proliferation of such data becomes evident. Therefore, there is an essential need for some techniques or methods to interact with data and extract useful information and patterns from them. Text data mining (TDM) is the process of extracting desired information out of mountains of textual data that are inherently unstructured, without the need to read them all. In this paper, we shed the light on the-state-of-the-art in text mining as an interdisciplinary field of several related areas. To facilitate the understanding of text data mining, this paper proposes a framework that visualises this field in a step-wise manner, taking into consideration the semantic of the extracted text. In addition, this paper surveys a number of useful applications and proposes a new approach for spam detection based on the proposed TDM framework.

Suggested Citation

  • Sana'a A. Alwidian & Hani A. Bani-Salameh & Ala'a N. Alslaity, 2015. "Text data mining: a proposed framework and future perspectives," International Journal of Business Information Systems, Inderscience Enterprises Ltd, vol. 18(2), pages 127-140.
    • Handle: RePEc:ids:ijbisy:v:18:y:2015:i:2:p:127-140
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    Cited by:

    1. Samuel Zanferdini Oliva & Livia Oliveira-Ciabati & Denise Gazotto Dezembro & Mário Sérgio Adolfi Júnior & Maísa Carvalho Silva & Hugo Cesar Pessotti & Juliana Tarossi Pollettini, 2021. "Text structuring methods based on complex network: a systematic review," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1471-1493, February.

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