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International Journal of Scientific & Technology Research

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IJSTR >> Volume 9 - Issue 2, February 2020 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Performance Analysis Of Ensemble Feature Selection Method Under SVM And BMNB Classifiers For Sentiment Analysis

[Full Text]

 

AUTHOR(S)

M.Gunasekar, Dr.S.Thilagamani

 

KEYWORDS

Text classification, feature selection, Gini index, Naïve Bayes, mRMR, SVM

 

ABSTRACT

The process of sentiment analysis identifies whether a given piece of text is positive, negative or neutral. It helps the business organizations to make use of the data in an effective way and to take informed decisions. It also saves human time and effort since this is an automated process. To automate the sentiment prediction the comment or review of a user has to be synthesized accurately. Since the feature selection is an important factor in sentiment prediction, this paper uses composite n-gram model with two feature selection methods mRMR (Minimum redundancy and Maximum Relevance) and improved Gini index. Then the sentiment prediction is carried out using SVM (Support Vector Machines) and BMNB (Binary multinomial Naïve Bayes) classifier. Experiment results shows that BMNB classifier performs better under both feature selection methods.

 

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