Volume & Issue no: Volume 3, Issue 4, July - August 2014
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Title: |
Analyzing Sentiment of Movie Review Data using Naive Bayes Neural Classifier |
Author Name: |
Lina L. Dhande and Dr. Prof. Girish K. Patnaik |
Abstract: |
Abstract
Now a days sentiment analysis is active field of research, to
extract people s opinion about particular product or service.
The most useful application of sentiment analysis is the
sentiment classification of product reviews. The task of
sentiment classification is to classify reviews of user as
positive or negative from textual information alone. For that
purpose many researchers used data mining classification
techniques such as Naive Bayes classifier with strong
independence assumption. But Naive Bayes classifier lack in
accuracy for many complex real-world situations where there
exists dependency among features. Further, the Neural
Network with appropriate network structure is good enough
to handle the correlation or dependence between input
variables. In proposed system, the Naive Bayes and Neural
Network classifier are combined for sentiment classification.
In Experimental results, the movie review is classified into
positive or negative polarities of sentiment using classifiers.
The accuracy of sentiment analysis is increased upto 80.65%
by combining Naive Bayes classifier with Neural Network for
unigram feature on movie review dataset.
Keywords: Sentiment Analysis, Sentiment Classification,
Naive Bayes, Neural Network Classifier. |
Cite this article: |
Lina L. Dhande and Dr. Prof. Girish K. Patnaik , "
Analyzing Sentiment of Movie Review Data using Naive Bayes Neural Classifier" , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) ,
Volume 3, Issue 4, July - August 2014 , pp.
313-320 , ISSN 2278-6856.
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