Perform Sentiment Analysis with Machine Learning Techniques
Ruchika Sharma

Ruchika Sharma, Computer Science Department, Chitkara University/ Baddi, Himachal Pradesh, India.
Manuscript received on December 11, 2013. | Revised Manuscript received on December 15, 2013. | Manuscript published on December 25, 2013. | PP: 59-62 | Volume-2 Issue-2, December 2013. | Retrieval Number: B0622122213/2013©BEIESP

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Abstract: Sentiment Analysis has become an indispensible part of product reviews in present scenario. We consider the problem of analyzing the overall sentiment of a document using Machine learning techniques. Sentiment Analysis is a very well studied field, but the scale remains limited to not more than a few hundred researchers. We improve the results using SVM kernel approach and compare the same with previously used techniques. The present research is a comparison and extension of the work proposed by Mullen and Collier (2003). Our system consists of a feature Extraction phase and a learning phase; on the basis of which the overall sentiment of the document is analyzed. Our present work uses the movie review data set used by Pang (2002). The present work shows that SVM Kernel approach outperforms the Naïve bayes approach.
Keywords: Sentiment Analysis, Classifier, SVM, td-idf, Naïve Bayes, PCA.