Survey of Text Mining Research Methods and Their Innovative Applicability

Authors

  • Mihaela Chistol
  • Mirela Danubianu Stefan cel Mare University

Keywords:

Text Mining Techniques; Classification; Clustering; Information Retrieval; Innovative Applications

Abstract

Humans are social beings who feel a strong need for communication. From the earliest times, the exchange of information was based on primary skills such as sight and speech. Thus, at the beginning of the 20th century, a famous phrase was uttered that claims that “A picture is worth a thousand words”. In the contemporary world, this phrase is no longer appropriate because with the discovery of the World Wide Web the textual revolution began. While digitalization continues at light speed, the need to process huge amounts of generated text resources is felt even more strongly. Therefore to solve the crisis of information overload, text mining is used, which is a new and interesting area of computer science research. This paper presents a methodological and conceptual theory of text mining along with the main methods behind it. Following an in-depth examination of the literature, the study shows the fundamental directions of text mining research such as classification, clustering, and information retrieval. In addition, the article presents state-of-the-art applications that implement the concept of text mining to solve problems in the real world.

References

Chakrabarti, S. (2002). Mining the Web Discovering knowledge from hypertext data. Bombay: Indian Institute of Technology.
Feldman, R. & Sanger, J. (2007). The text mining handbook. Advanced Approaches in Analyzing Unstructured Data. Cambridge: Cambridge University Press.
Kim Peek (2021). Wikipedia. https://en.wikipedia.org/wiki/Kim_Peek.
Kowsari, K.; Meimandi, K. J.; Heidarysafa, M.; Mendu, S.; Barnes, L. & Brown, D. (2019). Text Classification Algorithms: A Survey. Information.
Li, N. & Wu, D. (2010). Using text mining and sentiment analysis for online forums hotspot detection and forecast. Decis. Support Syst., pp. 354-368.
Miner, G.; Delen, D.; Elder, J.; Fast, A.; Hill, T. & Nisbet, R. & Balakrishnan, K. (2012). Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications. Waltham: Academic Press is an imprint of Elsevier.
*** (2020). Natural Language Processing (NLP). IBM Cloud Education: https://www.ibm.com/cloud/learn/natural-language-processing.
Singh, J. & Gupta, V. (2016). Text Stemming: Approaches, Applications, and Challenges. ACM Computing Surveys, pp. 1-46.

Downloads

Published

2021-11-02

How to Cite

Chistol, M., & Danubianu, M. . (2021). Survey of Text Mining Research Methods and Their Innovative Applicability: Array. Journal of Danubian Studies and Research, 11(1). Retrieved from https://dj.univ-danubius.ro/index.php/JDSR/article/view/1432

Issue

Section

Entrepreneurial Perspectives and their Impact on the Danube

Most read articles by the same author(s)