Opinion mining is a personal belief, view, perception, comments or expression on some topic ,person, product etc. In a current digital word every information is on Word Wide Web or digital form. With adventure of 4G mobile technology people use and share data or information on social media like twitter, face book, whatsapp application via mobile or computer. In order to take very quick and reasonable decision, it has become a common practice for decision maker to let their users review or express opinions about particular domain. A common user feels comfortable with the Internet, more and more opinions are writing. The number of opinions users receives is growing fast. Some popular products can get hundreds or thousands of reviews on some good trading venues. This makes it very difficult for a potential customer to read it to help determine if the product should be purchased or not. Therefore in this paper we introduce a simplified mobile computing opinion mining model which handles multilingual voice to text and text processing opinions. Moreover we compare accuracy result of existing Naïve bayes and SVM opinion mining algorithm result with our implemented model.

Opinion, Sentiment Analysis, Supervise Learning, Naïve Bayes, SVM, OM

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