Text Mining: Techniques, Applications and Issues

Rapid progress in digital data acquisition tech-niques have led to huge volume of data. More than 80 percent of today’s data is composed of unstructured or semi-structured data. The discovery of appropriate patterns and trends to analyze the text documents from massive volume of data is a big issue. Text mining is a process of extracting interesting and non-trivial patterns from huge amount of text documents. There exist different techniques and tools to mine the text and discover valuable information for future prediction and decision making process. The selection of right and appropriate text mining technique helps to enhance the speed and decreases the time and effort required to extract valuable information. This paper briefly discuss and analyze the text mining techniques and their applications in diverse fields of life. Moreover, the issues in the field of text mining that affect the accuracy and relevance of results are identified.

[1]  Rafeeq Al-Hashemi Text Summarization Extraction System (TSES) Using Extracted Keywords , 2010, Int. Arab. J. e Technol..

[2]  Dilanthi Amaratunga,et al.  Text Analytics for Android Project , 2014 .

[3]  Jinju Joby,et al.  Accessing Accurate Documents by Mining Auxiliary Document Information , 2015, 2015 Second International Conference on Advances in Computing and Communication Engineering.

[4]  Ian H. Witten,et al.  Text mining in a digital library , 2004, International Journal on Digital Libraries.

[5]  Maria Skeppstedt,et al.  Synonym extraction and abbreviation expansion with ensembles of semantic spaces , 2014, Journal of Biomedical Semantics.

[6]  Abdul Razak Hamdan,et al.  Immune based feature selection for opinion mining , 2013 .

[7]  M. Chidambaram,et al.  Text Mining: Concepts, Applications, Tools and Issues - An Overview , 2013 .

[8]  Ralf Steinberger,et al.  A survey of methods to ease the development of highly multilingual text mining applications , 2011, Language Resources and Evaluation.

[9]  Jing Zhao,et al.  Ensembles of randomized trees using diverse distributed representations of clinical events , 2016, BMC Medical Informatics and Decision Making.

[10]  D. Sujatha,et al.  IMPROVED METHOD FOR PATTERN DISCOVERY IN TEXT MINING , 2013 .

[11]  David Contreras,et al.  Evaluation of semantic similarity metrics applied to the automatic retrieval of medical documents: An UMLS approach , 2016, Expert Syst. Appl..

[12]  Wu He,et al.  Examining students' online interaction in a live video streaming environment using data mining and text mining , 2013, Comput. Hum. Behav..

[13]  Tong Zhang,et al.  Text Mining: Predictive Methods for Analyzing Unstructured Information , 2004 .

[14]  Ramesh Sharda,et al.  Information Extraction from Interviews to Obtain Tacit Knowledge: A Text Mining Application , 2009, AMCIS.

[15]  C. L. Philip Chen,et al.  Data-intensive applications, challenges, techniques and technologies: A survey on Big Data , 2014, Inf. Sci..

[16]  Rajendra Kumar Roul,et al.  A Novel Modified Apriori Approach for Web Document Clustering , 2015, ArXiv.

[17]  Chris H. Q. Ding,et al.  Minimum Redundancy Feature Selection from Microarray Gene Expression Data , 2005, J. Bioinform. Comput. Biol..

[18]  William R. Hersh,et al.  A survey of current work in biomedical text mining , 2005, Briefings Bioinform..

[19]  B Lakshmmi Narayana,et al.  A New Clustering Technique On Text In Sentence For Text Mining , 2015 .

[20]  Weiguo Fan,et al.  Tapping the power of text mining , 2006, CACM.