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Weighted Summarization of Student Feedback using Sentiment Analysis

by Sneha, B. Akshatha Bhat, Preetham Kumar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 97 - Number 3
Year of Publication: 2014
Authors: Sneha, B. Akshatha Bhat, Preetham Kumar
10.5120/16984-7087

Sneha, B. Akshatha Bhat, Preetham Kumar . Weighted Summarization of Student Feedback using Sentiment Analysis. International Journal of Computer Applications. 97, 3 ( July 2014), 1-8. DOI=10.5120/16984-7087

@article{ 10.5120/16984-7087,
author = { Sneha, B. Akshatha Bhat, Preetham Kumar },
title = { Weighted Summarization of Student Feedback using Sentiment Analysis },
journal = { International Journal of Computer Applications },
issue_date = { July 2014 },
volume = { 97 },
number = { 3 },
month = { July },
year = { 2014 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume97/number3/16984-7087/ },
doi = { 10.5120/16984-7087 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:23:06.577000+05:30
%A Sneha
%A B. Akshatha Bhat
%A Preetham Kumar
%T Weighted Summarization of Student Feedback using Sentiment Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 97
%N 3
%P 1-8
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Every year massive amount of feedback is gathered from students regarding subjects and its respective faculty. The amount of time to analyze this data manually is a very tedious and time consuming. This is where the summarization feature comes into picture. It extracts important information found in every feedback document. Automatic summarization based on word frequency statistics takes comments and weights them to produce word frequency and then sentence frequency. Also, the sentiment information in these documents belongs to a wide spectrum ranging from positive to negative. SentiWordNet assigns sentiment numerical scores: positive or negative. Thus, providing clues for sentiment analysis. The spell-checker helps to rectify the incorrect words for proper implementation of those two concepts.

References
  1. Levenshtein distance. http://en. wikipedia. org/wiki/ Levenshtein_distance.
  2. R. Feldman. Techniques and Applications for Sentiment Analysis. http://cacm. acm. org/magazines/2013/4/ 162501-techniques-and-applications-for-sentiment-analysis/ fulltext, 2013.
  3. M. Guerini, L. Gatti, and M. Turchi. Sentiment analysis: How to derive prior polarities from sentiwordnet. September 2013.
  4. W. Hong, S. Jiang, H. Wang, and J. Shi. Weighted-based summarization of music comments. The 8th International Conference on Computer Science and education(ICCSE'13), April 2013.
  5. Aditya Joshi. Simple Spell-Checker in JAVA. http: //bakedcircuits. wordpress. com/2013/08/10/ simple-spell-checker-in-java/, 2013.
  6. Dan Jurafsky. Minimum Edit Distance. http://web. stanford. edu/class/cs124/lec/med. pdf, 2014.
Index Terms

Computer Science
Information Sciences

Keywords

Spell-checker Sentiment Analysis Text summarization

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