Text Mining Techniques for Information Extraction: Issues and Applications
DOI:
https://doi.org/10.26438/ijcse/v7i1.944950Keywords:
Information extraction, Information Retrieval, NLP, Text mining, ApplicationsAbstract
ext mining research area has become very popular among researchers from various disciplines today. Text mining is one of the major areas of research for natural language documents. In this review, a comprehensive introduction and overview of text mining and existing research status is discussed. The major issue in text mining is the discovery of relevant information and patterns that are used to analyze text documents from the huge volume of information available over the internet. A number of tools and numerous methods exist for determining the relevant text and identifying valuable information for future research analysis and decision making. The correct and effective methods and tools for text mining helps in speed up the extraction of valuable information and it also decreases the effort and time required for the analysis. The paper describes the methods, applications and issues of text mining in various fields of life. These results based on the text mining information from the various cited research articles and publications will be very useful for the researchers working in this research area. In addition, various issues related to text mining are identified that affect the accuracy and relevancy of results.
References
[1] Sumathy K.L. & Chidambaram M. Text Mining: Concepts, Applications, Tools and Issues – An Overview, International Journal of Computer Applications (0975 – 8887), 80(4), 29-32, 2013.
[2] Ah-Hwee Tan. Text Mining: The state of the art and the challenges. Procedings of the pakdd 1999 workshop on knowledge discover from advance data bases. 1999.
[3] Gupta V. & Lehal Gurpreet S. A Survey of Text Mining Techniques and Applications, Journal Of Emerging Technologies In Web Intelligence, 1(1),60-76, 2009.
[4] Duriau, V.J., Reger, K.R., & Pfarrer, M.D. A content analysis of the content analysis literature in organization studies: research themes, data sources, and methodological refinements. Organizational Research Methods, 10 (1), 5–34, 2007.
[5] Jamiy, F.E., Daif, A., Azouazi, M., & Marzak, A. The potential and challenges of big data – recommendation systems next level application. arXiv preprint arXiv:1501.03424. 2015.
[6] Kobayashi, V.B., Mol, S.T., Berkers, H.A., Kismihók, G., & Den Hartog, D.N. Text classification for organizational researchers: a tutorial. Organizational Research Methods, 21(3), 766–799, 2018.
[7] Janasik, N., Honkela, T., & Bruun, H. Text mining in qualitative research: application of an unsupervised learning method. Organizational Research Methods, 12 (3), 436–460, 2009.
[8] Wiedemann, G. Opening up to big data: computer- assisted analysis of textual data in social sciences.Historical Social Research/Historische Sozialforschung, 38(4), 332–357, 2013.
[9] Arvinder Kaur & Deepti Chopra . Comparison of Text Mining. 5th Internaional conferenceon Relaibility,info com technology & optimization (Trends & Fture directions), 2016.
[10] Henriksson A., Zhao J., Dalianis H., & Bostrom H. Ensembles of randomized trees using diverse distributed representations of clinical events, BMC Medical Informatics and Decision Making, 16 (2), 69-78, 2016.
[11] Solanki H. Comparative study of data mining tools and analysis with unified data mining theory,International Journal of Computer Applications, 75(16), 2013.
[12] Kaklauskas A., Seniut M., Amaratunga D., Lill I., Safonov A., Vatin N., Cerkauskas J., Jackute I., Kuzminske A., & Peciure L. Text analytics for android project, Procedia Economics and Finance, 18, 610–617, 2014.
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