CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

An Extractive Text Summarization approach for Analyzing Educational Institution’s Review and Feedback Data

by Jai Prakash Verma, Atul Patel
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 143 - Number 6
Year of Publication: 2016
Authors: Jai Prakash Verma, Atul Patel
10.5120/ijca2016910233

Jai Prakash Verma, Atul Patel . An Extractive Text Summarization approach for Analyzing Educational Institution’s Review and Feedback Data. International Journal of Computer Applications. 143, 6 ( Jun 2016), 51-55. DOI=10.5120/ijca2016910233

@article{ 10.5120/ijca2016910233,
author = { Jai Prakash Verma, Atul Patel },
title = { An Extractive Text Summarization approach for Analyzing Educational Institution’s Review and Feedback Data },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2016 },
volume = { 143 },
number = { 6 },
month = { Jun },
year = { 2016 },
issn = { 0975-8887 },
pages = { 51-55 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume143/number6/25085-2016910233/ },
doi = { 10.5120/ijca2016910233 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:45:40.322225+05:30
%A Jai Prakash Verma
%A Atul Patel
%T An Extractive Text Summarization approach for Analyzing Educational Institution’s Review and Feedback Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 143
%N 6
%P 51-55
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Big Data analytics helps the enterprises and institutions to understand and identify the usability of large amount of data generated by their routine operations. All most third forth part of these types of data is semi-structured text data. Many types of actionable insights can be found from these type of semi-structured text data that can help strategic management for making right decision. In this paper we are proposing a recommendation system model for understanding and finding actionable insight from the large amount of text data generated for an educational institution. Here we are discussing different type of data generation sources of this types of data as well as data cleaning processes required. The wordcloud for an educational institution is published that help strategic management for understanding the sentiment of different stack holders mainly students. Herewith we are identifying different types of findings from these sets of words that helps for betterment in the functionaries of an Educational Institution.

References
  1. A. Ittoo, et al., Text analytics in industry: Challenges, desiderata and trends, Comput. Industry (2016), http://dx.doi.org/10.1016/j.compind.2015.12.001
  2. Zheng Xiang, Zvi Schwartz, John H. Gerdes Jr., Muzaffer Uysal, “What can big data and text analytics tell us about hotel guestexperience and satisfaction?”, International Journal of Hospitality Management 44 (2015) 120–130 , 0278-4319/© 2014 Elsevier
  3. Venkat N. Gudivada, Dhana Rao, Vijay V. Raghavan, “Big Data Driven Natural Language Processing Research and Applications”, Chapter 9, Handbook of Statistics, Vol. 33. http://dx.doi.org/10.1016/B978-0-444-63492-4.00009-5 © 2015 Elsevier B.V
  4. Suvarna D. Tembhurnikar, Nitin N. Patil, “Topic Detection using BNgram Method and Sentiment Analysis on Twitter Dataset”, 978-1-4673-7231-2/15 ©2015 IEEE
  5. Weiyi Ge, Chang Liu, Shaoqian Zhang, and Xin Xu, “Summarizing Events from Massive News Reports on the Web”, 2015 International Conference on Network and Information Systems for Computers, 978-1-4799-1843-0/15 © 2015 IEEE
  6. Jai Prakash Verma, Bankim Patel, and Atul Patel, “Big Data Analysis: Recommendation System with Hadoop Framework”, 2015 IEEE International Conference on Computational Intelligence & Communication Technology, 978-1-4799-6023-1/15 © 2015 IEEE
  7. LI Bing, Keith C.C. Chan, “A Fuzzy Logic Approach for Opinion Mining on Large Scale Twitter Data”, 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing, 978-1-4799-7881-6/14 © 2014 IEEE
  8. Yogesh Sankarasubramaniam, Krishnan Ramanathan, Subhankar Ghosh, “Text summarization using Wikipedia”, Information Processing and Management 50 (2014) 443–461, 0306-4573/_ 2014 Elsevier
  9. Amir Gandomi∗, Murtaza Haider, “Beyond the hype: Big data concepts, methods, and analytics”, International Journal of Information Management 35 (2015) 137–144, 0268-4012/© 2014 The Authors. Published by Elsevier Ltd
  10. Rafael Ferreira, Luciano de Souza Cabral, Rafael Dueire Lins, Gabriel Pereira e Silva, Fred Freitas, George D.C. Cavalcanti a, Rinaldo Lima a, Steven J. Simske b, Luciano Favaro“, Assessing sentence scoring techniques for extractive text summarization”, Expert Systems with Applications 40 (2013) 5755–5764, _ 2013 Elsevier
  11. Elena Lloret, Manuel Palomar, “Tackling redundancy in text summarization through different levels of language analysis”, Computer Standards & Interfaces 35 (2013) 507–518, © 2012 Elsevier
  12. Tushar Ghorpade, Lata Ragha, “Hotel Reviews using NLP and Bayesian Classification”, 2012 International Conference on Communication, Information & Computing Technology (ICCICT), Oct. 19-20, Mumbai, India
  13. Derek Bridge, Paul Healy, “The GhostWriter-2.0 Case-Based Reasoning system for making content suggestions to the authors of product reviews”, Knowledge-Based Systems 29 (2012) 93–103, © 2011 Elsevier
  14. Xintian Yang, Amol Ghoting, and Yiye Ruan, “A Framework for Summarizing and Analyzing Twitter Feeds, KDD’12, August 12–16, 2012, Beijing, China., Copyright 2012 ACM 978-1-4503-1462-6 /12/08”
  15. Jai Prakash Verma, Bankim Patel, and Atul Patel, “Web Mining: Opinion and Feedback Analysis for Educational Institutions”, International Journal of Computer Applications (0975 – 8887) Volume 84 – No 6, December 2013
  16. (2016). Frequent Pattern Mining - spark.mllib, [Online]. Available: https://spark.apache.org/docs/latest/mllib-frequent-pattern-mining.html
Index Terms

Computer Science
Information Sciences

Keywords

Big Data Big Data Analytics Extractive text summarization Educational Word Cloud Text Analytics Hadoop Framework Applications.

Powered by PhDFocusTM