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Reseach Article

Sentiment Classification using Subjective and Objective Views

by Suke Li
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 80 - Number 7
Year of Publication: 2013
Authors: Suke Li
10.5120/13875-1749

Suke Li . Sentiment Classification using Subjective and Objective Views. International Journal of Computer Applications. 80, 7 ( October 2013), 30-34. DOI=10.5120/13875-1749

@article{ 10.5120/13875-1749,
author = { Suke Li },
title = { Sentiment Classification using Subjective and Objective Views },
journal = { International Journal of Computer Applications },
issue_date = { October 2013 },
volume = { 80 },
number = { 7 },
month = { October },
year = { 2013 },
issn = { 0975-8887 },
pages = { 30-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume80/number7/13875-1749/ },
doi = { 10.5120/13875-1749 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:53:55.758186+05:30
%A Suke Li
%T Sentiment Classification using Subjective and Objective Views
%J International Journal of Computer Applications
%@ 0975-8887
%V 80
%N 7
%P 30-34
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This work proposes a new semi-supervised sentiment classification method by exploiting a large number of unlabeled instances to conduct sentiment classification for Web consumer reviews. In the proposed method every consumer review has two views: subjective view and objective view. The subjective view of a consumer review reflects the opinions expressed by opinion words, while the objective view is constructed by the remaining text features. This work is trying to combine two kinds of views to carry out sentiment classification. The method is based on the co-training framework which needs three basic sentiment classifiers to iteratively get the final sentiment classifier. In the proposed method, the first sentiment classifier is constructed using the common unigram features coming from consumer reviews. The second sentiment classifier is trained on the subjective views constructed by opinion words extracted from consumer reviews. The remaining text features of these reviews are used for obtaining the objective views which can be trained for the third classifier. Experimental results show the proposed method is effective, and it has better performance than the Self-learning SVM method.

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Index Terms

Computer Science
Information Sciences

Keywords

Sentiment Classification Opinion Mining Text Mining Web Mining Data Mining.