A Survey on Text Analytics and Text Mining
C.P. Thamil Selvi1, Pushpa Laksmi2
1C. P Thamil Selvi, Associate Professor, Department of Computer Science and Engineering, Sri Ranganathar Institute of Engineering and Technology, Athipalayam, Coimbatore (Tamil Nadu), India.
2Dr. Pushpa Lakshmi, Professor, Department of Information Technology, PSNA College of Engineering and Technology, Dindigul (Tamil Nadu), India.
Manuscript received on 26 March 2019 | Revised Manuscript received on 05 April 2019 | Manuscript Published on 27 April 2019 | PP: 567-578 | Volume-7 Issue-6S2 April 2019 | Retrieval Number: F90820476S219/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Text analytics is rapidly growing day by day in computing world. Text analytics and text mining is a necessary process integrated with the several recent research areas like information retrieval and computational linguistics. Generally natural language process is used for knowledge extraction from ontological data generated and written from various sources and human beings respectively. To proceed with text mining, text analytics is important to analyse the text data, because it is in the form of unstructured. Several recent research works were focused on implementing an efficient text analytics and mining approaches based on various dataset. But, still there is a need of improving the accuracy of classification and mining over text data. Before going to design and implement a text analytics and mining method, it is essential to understand the issues and challenges met by earlier research works. Also, social networks and other communication networks, people are sharing their own pattern of text which has spelling error, grammatical error and sentence error, needs to be corrected. Hence, text analyzation and mining are a complicated task from social network data. Some of the review documents discussed about information extraction and some emphasized about different applications of text mining. But, most of the earlier review and survey documents do not target especially on the social networking data sets. The goal of this paper is presenting a detailed survey of several text analytics, and text mining methods based on the social networks. Also, this survey examines and explores the recent emerging methods used in the text analytics domain. Finally, it has been provided a summarized report for easy and fast understanding the various issues and challenges in text analytics and mining. It helps to find out a solution for big data analytics.
Keywords: Text Analytics, Text Mining, Social Network Data, Twitter Dataset, Feature Extraction, Classification, Optimization Algorithms.
Scope of the Article: Text Mining