SENTIMENT ANALYSIS, A SUPPORT VECTOR MACHINE MODEL BASED ON SOCIAL NETWORK DATA

Abstract

In this paper, we analyze the efficacy linguistic features in social media review to detecting the sentiment of any message. To calculate the sentiment polarity, we use a machine-learning method that applies text-categorization techniques to word vector of text in the document. We estimate the advantage of not discarding expressions of the informal and creative language used in micro blogging. The new features in this paper are the pre-classified data source which increase the calculation accuracy.