Survey of Text Mining Research Methods and Their Innovative Applicability
Keywords:
Text Mining Techniques; Classification; Clustering; Information Retrieval; Innovative ApplicationsAbstract
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
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.
Published
How to Cite
Issue
Section
License
The author fully assumes the content originality and the holograph signature makes him responsible in case of trial.