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Sentiment Analysis Based Mining and Summarizing Using SVM-MapReduce


Jayashri Khairnar, Mayura Kinikar


Vol. 15  No. 4  pp. 90-94


The Sentiment Analysis is the process use to determine the semantic orientation of the reviews. There are many algorithms are exists for the sentiment classification. Support vector machines are a specific type of machine learning algorithm used for many statistical learning problems, such as text classification, spam filtering, face and object recognition, handwriting analysis and countless others. We have studied the SVM as the recent machine learning method for sentiment classification, this method later suppressed by using feature extraction method. We find a way to reduce the size of summary using LSA feature extraction method. In this paper we are extending and investigating the SVM method by addition of the parallel processing methods of sentiment classification such as MapReduce and Hadoop. The practical evaluation of SVM with and without MapReduce as well as LSA is presented in this paper.


Sentiment Analysis, Support Vector Machine (SVM), Feature Extraction, Latent Semantic Analysis (LSA), MapReduce, Hadoop.