Sentiment analysis of smart phone product review using SVM classification technique

There is a massive increase in number of people who access various social networking and micro-blogging websites that gives new shape to the impression of today's generation. Several reviews for a specific product, brand, individual personality, forum sand movies etc. are very helpful in directing the perception of people. Hence the analysts are commenced to create algorithms to automate the classification of distinctive reviews on the basis of their polarities particularly: Positive, Negative and Neutral. This automated classification mechanism is referred as Sentiment Analysis. The ultimate aim of this paper is to apply Support Vector Machine (SVM) classification technique to classify the sentiment sand texts for smart phone product review that analyses different datasets used for classification of sentiments and texts. Furthermore, various data sets have been utilized for training as well as testing and implemented using Support Vector Machine (SVM) to investigate polarity of the ambiguous tweets. The experimental work includes three performance features such as Precision, Recall and F-measure. On the basis of these features, the accuracy of the different products has been computed. The obtained result approves high accuracy as predicted on the basis of smart phone reviews.