Sentiment Classification of text reviews using novel feature selection with reduced over-fitting

Sentiment Classification is an important and hot current research area. This extended abstract of our work observes the effect of some machine learning algorithms like Naïve Bayes, SVM and their variants on the movie review data. We have used a novel and hybrid feature selection/reduction technique...

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Bibliographic Details
Main Authors: Siva RamaKrishna Reddy, V, Somayajulu, D V L N, Dani, A R
Format: Conference Proceeding
Language:English
Subjects:
Algorithm design and analysis
Niobium
Support vector machines
Online Access:Request full text
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Summary:Sentiment Classification is an important and hot current research area. This extended abstract of our work observes the effect of some machine learning algorithms like Naïve Bayes, SVM and their variants on the movie review data. We have used a novel and hybrid feature selection/reduction technique which is minimizing the number of features exponentially. The results show that with our feature selection procedure there is an improvement in classification efficiency compared to the previous work and with reduced over-fitting.