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Feature Level Opinion Mining of Educational Student Feedback Data using Sequential Pattern Mining and Association Rule Mining

by Ayesha Rashid, Sana Asif, Naveed Anwer Butt, Imran Ashraf
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 81 - Number 10
Year of Publication: 2013
Authors: Ayesha Rashid, Sana Asif, Naveed Anwer Butt, Imran Ashraf
10.5120/14050-2215

Ayesha Rashid, Sana Asif, Naveed Anwer Butt, Imran Ashraf . Feature Level Opinion Mining of Educational Student Feedback Data using Sequential Pattern Mining and Association Rule Mining. International Journal of Computer Applications. 81, 10 ( November 2013), 31-38. DOI=10.5120/14050-2215

@article{ 10.5120/14050-2215,
author = { Ayesha Rashid, Sana Asif, Naveed Anwer Butt, Imran Ashraf },
title = { Feature Level Opinion Mining of Educational Student Feedback Data using Sequential Pattern Mining and Association Rule Mining },
journal = { International Journal of Computer Applications },
issue_date = { November 2013 },
volume = { 81 },
number = { 10 },
month = { November },
year = { 2013 },
issn = { 0975-8887 },
pages = { 31-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume81/number10/14050-2215/ },
doi = { 10.5120/14050-2215 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:56:11.347601+05:30
%A Ayesha Rashid
%A Sana Asif
%A Naveed Anwer Butt
%A Imran Ashraf
%T Feature Level Opinion Mining of Educational Student Feedback Data using Sequential Pattern Mining and Association Rule Mining
%J International Journal of Computer Applications
%@ 0975-8887
%V 81
%N 10
%P 31-38
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This research paper combines the data mining with natural language processing to extract the nuggets of knowledge from massive volume of student feedback dataset on faculty performance. The main objective is to compare two renowned association rule mining and sequential pattern mining algorithms namely Apriori and Generalized Sequential Pattern (GSP) mining in the context of extracting frequent features and opinion words. Student feedback data crawled, pre-process and tagged, then convert in tri-model data files. Both algorithms are applied on prepared data through WEKA 3. 7. 10 (a machine learning tool) to extract the rules. Mined rules are applied on testing files to extract frequent features and opinion words. Evaluated Results show that GSP is more significant to use for textual data mining than Apriori.

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Index Terms

Computer Science
Information Sciences

Keywords

Opinion Mining Sentence Level Sentiment Classification Sequential Pattern Mining Apriori Generalized Sequential Pattern Opinion Words Frequent Features

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