Basic review of different strategies for sentiment analysis in online social networks

Abstract

The growth of different online networks such as MySpace, Twitter, LinkedIn and Face book have been increased in recent years, high amount of data outsource via social media into data sources. This huge amount of data analyzed for research on different types of real time applications. So that analysis of sentiment and mining user opinion is one of aggressive concept to explore meaning of outsourced data. While different types of approaches are implemented to identifying sentiment and opinion in social networks like pattern based classification with respect to parts of speech, emotions and batch model learning while analyzing huge amount of data. In this paper we give brief description of different machine learning approaches to describe utilize sentiment of huge amount data in social networks. We give survey of different approaches with respect to sentiment exploration from online social network. Also describe comparative analysis of different methods used for analysis of sentiment and mining of user opinion in online social networks.