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

by Jai Prakash Verma, Atul Patel
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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
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Index Terms

Computer Science
Information Sciences

Keywords

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

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