Email this article 
Open Access
Subscription or Fee Access
A Comprehensive Study of Text Analytics
Abstract
The interactions between human languages and computers is termed as Natural Language Processing. These interactions are between the fields of computer science, artificial intelligence, and computational linguistics. As it involves human languages, so the term NLP is referred. NLP requires the expertise in statistics, linguistics, expert systems etc. Basically it aims at text analysis by using automation methods. On the other hand, customer relationship management is an integral part of any organization where in it is structured to fetch all the relevant data pertaining to its customer's viz., customer profile, customer feedback. Transactions on all customer activities etc. The paper aims at the comprehensive study of text analytics using natural language processing. The natural language processing tools can be utilized to identify customer pain points to improve customer relationship management of any organization, an experiment is conducted to use statistical and graphical visualization to arrive at a conclusion by using various techniques of text mining using open source tool.
Keywords
References
Adrian Payne, Pennie Frow, "A Strategic Framework for Customer Relationship Management", Journal of Marketing, Vol. 69, No. 4, pp., 167-176, October 2005.
Amir D Aczel., Jayavel Sounderpandian., Palanisamy Saravanan & Rohit Joshi (7th edition) (2012) “Complete Business Statistics”.
Alec Go, Richa Bhayani, Lei Huang “Twitter Sentiment Classification using Distant Supervision” Stanford University Stanford, CA 94305
Basavaraj N. Hiremath., & Dr Malini M. Patil. "“Customer Relationship Management Through Natural Language Processing Using Text Analytics” , Technical article in CSI communications magazine ,Vol 40, No 1, pp.,11-13, April 2016.
C.D. Athuraliya1. M.K.H. Gunasekara., Srinath Perera., & Sriskandarajah Suhothayan. “Real-time Natural Language Processing for Crowd sourced Road Traffic Alerts” WSO2 Inc., Colombo, Sri Lanka.
E.W.T. Ngaia., Li Xiub, D.C.K. Chaua “Application of data mining techniques in customer relationship management” a literature review and classification Expert Systems with Applications Vol. 36, No. 2, pp., 2592-2602, March 2009.
“Gartner Magic Quadrant for the CRM Customer Engagement Center” published by Gartner, Inc. February 2015.
Gordon S. Linoff., & Michael J. A. Berry. (3rd Ed). (2012) “Data Mining Techniques”.
Graham. Williams.,“Hands-On Data Science with R Text Mining”. January 2016.
http://www.oracle.com/us/corporate/profit/features/050113-swa-1946338.html.
https://cran.rproject.org/web/packages/wordcloud/wordcloud.pdf, February 2015.
Injazz J. Chen, Karen Popovich, "Understanding customer relationship management (CRM): People, process and technology", Business Process Management Journal, Vol. 9, No. 5, pp.672 – 688.
Jiawei Han, Micheline kamber & Jian pei) “Data mining concepts and techniques”. 3rd edition , 2012
Jiawei Han, Micheline kamber “Data Mining concepts and Techniques”. 2nd edition, 2006.
Joseph Turian, Ph.D., (2015) “IBM Watson Thought leadership white paper Text analysis: A crucial part of enterprise data initiatives”.
Liang Chen, Naoyuki Tokuda, Akira Nagai. “A Differential LSI Method for Document Classification”. Proceedings of International conference on face and gesture recognition p.30, April 14-16, 1998.
Marc A van Driel1, Jorn Bruggeman, Gert Vriend, Han G Brunner and Jack A M Leunissen "A text-mining analysis of the human phenome", European Journal of Human Genetics, Vol. 4, No.14, pp., 535–542. February 2006.
Michaeal J. A. Berry., & Gordson S, Linoff. “Text book of Mastering data mining,” by Wiley India edition, 2012.
Michael Collins “Course notes for NLP” Columbia University.
Oracle CX., Customer experience solutions from https:// www.oracle.com / applications/customer-experience.
Paul Teetor., (3rd Edition). (2012, August). “R cook book,” Oreilly publications, Scholl of Computer Science and Information technology RMIT University Melbourne, Australia.
Peter F. Brown Peter V. DeSouza, Robert L. Mercer Vincent J. Della Pietra Jenifer C. Lai," Class-based n-gram models of natural language" , Journal of Computational Linguistics, Vol. 18, No.4, pp., 467-479 December 1992.
Phil Blunsom (2014, August), “Hidden Morkov models”
Ross Ihaka., & Robert Gentleman, “R: A language for Data Analysis and Graphs” , Journal of Computational and Graphical Statistics. Vol. 5, No. 3pp. 299-314.
Shuang Dong., & Zhihong Wang. “Evaluating Service Quality in Insurance Customer Complaint Handling throught Text Categorization” School of Management Beijing Union University, BUU Beijing, China
Stephen Wan., Cecile Paris., & Dimitrios Georgakopoulos. “Social Media Data Aggregation and Mining for Internet-Scale Customer Relationship Management”, Proceedings of IEEE 16th International Conference on Information Reuse and Integration, 2015.
Suvarna D. Tembhurnikar., & Nitin N. Patil. “Topic Detection using BNgram Method and Sentiment Analysis on Twitter Dataset” Department of Computer Engineering, North Maharashtra University MS, India.
Terry Winograd, "Understanding Natural Language", New York: Academic Press, 1972. Also published in Cognitive Psychology, Vol. 3 No. 1 pp. 1-191, 1972
Tetsuya Nasukawa, Yamato-shi, Kanagawa-ken, Jeonghee Yi, "Sentiment analysis: capturing favorability using natural language processing", Proceedings of the 2nd international conference on Knowledge capture, 70-77 ACM New York, NY, USA.
Xiangyu Duan, Jun Zhao, and Bo Xu , "Probabilistic Models for Action-Based Chinese Dependency Parsing", ECML 2007, LNAI 4701, pp. 559–566, 2007. © Springer-Verlag Berlin Heidelberg 2007.
Yoav Goldberg., & Jon Orwant “A Dataset of Syntactic-Ngrams over Time from a Very Large Corpus of English Books,” Bar Ilan University Google Inc.s
Vidya K A and G, Aghila, "A Survey of Naïve Bayes Machine Learning approach in Text Document Classification", International Journal of Computer Science and Information Security, Vol. 7, No. 2, 2010.
Refbacks
- There are currently no refbacks.