Notification: Our email services are now fully restored after a brief, temporary outage caused by a denial-of-service (DoS) attack. If you sent an email on Dec 6 and haven't received a response, please resend your email.
CFP last date
20 December 2024
Reseach Article

Feature Ranking in Sentiment Analysis

by Maryam K. Jawadwala, Seema Kolkur
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 94 - Number 13
Year of Publication: 2014
Authors: Maryam K. Jawadwala, Seema Kolkur
10.5120/16407-6150

Maryam K. Jawadwala, Seema Kolkur . Feature Ranking in Sentiment Analysis. International Journal of Computer Applications. 94, 13 ( May 2014), 42-49. DOI=10.5120/16407-6150

@article{ 10.5120/16407-6150,
author = { Maryam K. Jawadwala, Seema Kolkur },
title = { Feature Ranking in Sentiment Analysis },
journal = { International Journal of Computer Applications },
issue_date = { May 2014 },
volume = { 94 },
number = { 13 },
month = { May },
year = { 2014 },
issn = { 0975-8887 },
pages = { 42-49 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume94/number13/16407-6150/ },
doi = { 10.5120/16407-6150 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:17:36.202263+05:30
%A Maryam K. Jawadwala
%A Seema Kolkur
%T Feature Ranking in Sentiment Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 94
%N 13
%P 42-49
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

With the rapid expansion of e-commerce over the past 15 years, more products are sold on the Web. More and more people are buying products online. In order to enhance customer shopping experience, it has become a common practice for online merchants to enable their customers to write reviews on products that they have purchased. Some popular products can get hundreds of reviews or more at some large merchant sites. Manual analysis of customer opinions is only possible to a certain extent and very time-consuming due to the multitude of contributions. From the e-commerce perspective, receiving consumer's feedback can greatly improve its strategies in order to increase products of the sector. This research work will present feature wise sentiment analysis of customer review. The goal of feature level sentiment analysis is to produce a feature-based opinion summary of multiple reviews. With summaries of opinions and features of the product, people can make effective decisions in less time. Such mining can be helpful for competitive marketing. Feature extraction can be performed using two approaches. Rule-based algorithm and HAC algorithm. Feature ranking will be done using MAX opinionscore algorithm and opinion score obtained from SentiWordNet.

References
  1. Tanvir Ahmad , Mohammad Najmud Doja,Ranking System for Opinion Mining of Features from Review Documents, IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 4, No 1, July 2012 , ISSN (Online): 1694-0814.
  2. Tanvir Ahmad , Mohammad Najmud Doja, Rule Based System for Enhancing Recall for Feature Mining from Short Sentences in Customer Review Documents, IJCSI International Journal of Computer Science Issues, Vol. 4 No. 06 June 2012, ISSN : 0975-3397.
  3. Magdalini Eirinaki, Shamita Pisal1, Japinder Singh2, Feature-based Opinion Mining and Ranking, Elsevier, Journal of Computer and System Sciences 78 (2012) 1175–1184, 3 November 2011.
  4. Seyed Hamid Ghorashi, Roliana Ibrahim2, Shirin Noekhah and Niloufar Salehi Dastjerdi ,A Frequent Pattern Mining Algorithm for Feature Extraction of Customer Reviews IJCSI nternational Journal of Computer Science Issues, Vol. 9, Issue 4, No 1, July 2012 , ISSN, (Online): 1694-0814.
  5. J. Pei, J. Han, H. Lu, S. Nishio, S. Tang 0and D. Yang, H-Mine: Fast and space-pre serving frequent pattern mining in large databases, "IIE TRANSACTIONS, pp. 593-605, 2007.
  6. A. Esuli, and F. Sebastiani, "SentiWordNet: A publicly available lexical resource for opinion mining", in Proceedings of LREC-06, the 5th Conference on Language Resources and Evaluation, Genova, IT, 2006, pp. 417-422.
  7. Avani Jadeja, Prof. Indr jeet Rajput,Feature Based Sentiment Analysis On Customer Feedback: A Survey, International Journal of Engineering Research & Technology (IJERT) ,Vol. 2 Issue 4, April – 2013, ISSN: 2278-0181.
  8. Gandhi Vaibhav C. ,Neha Pandya,Feature Level Text Categorization For Opinion Mining, International Journal of Engineering Research & Technology (IJERT) Vol. 2 Issue 5, May - 2013 ISSN: 2278-0181.
  9. B. Pang, B. and L. Lee, "A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts", in Proceedings of ACL 2004, 2004, pp. 271-278.
  10. Alaa Hamuda Mohamed Rohaim Department of Systems and Computers Engineering Al_Azhar,Reviews Classification Using SentiWordNet Lexicon, The Online Journal on Computer Science and Information Technology (OJCSIT), Reference Number: W11-0123.
  11. Stanford log-linear part-of-speech tagger, 2010, http://nlp. stanford. edu/software/tagger. shtml.
  12. M. Hu, and B. Liu, "Mining and Summarizing Customer Reviews", in Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'04), USA, 2004, pp. 168 – 177.
Index Terms

Computer Science
Information Sciences

Keywords

Sentiment analysis Opinion mining Feature ranking Natural language processing