Sentiment processing of social media information from both wireless and wired network

Recent years, information spreading under the environment of wireless communication has attracted increasing interest. Microblog platform on mobile terminals, as one product of wireless communication, facilitate information spreading and evolution by conveying message from peer to peer. Furthermore, sentiments from microblog reflect the attitude of peers on goods or events. Analysis of the sentiment can help in decision-making. Research work focuses on analyzing sentiment orientation for specific aspects of product with explicit names. However, it is not suitable for sentiment analysis of events using microblog data since users prefer to express their feelings in individual ways, namely the same object may be expressed in several ways. In this paper, a framework is proposed to calculate sentiment for aspects of events. First, we introduce some effective technologies in processing natural language, such as wordvec, HMM, and TextRank. Then, based on the state-of-art technologies, we build up a flowchart to get sentiment for aspects of events. At last, experiments are designed to prove these technologies on computing sentiment. During the process, name entities with the same meaning are clustered and sentiment carrier is filtered, with which sentiment can be got even users express their feeling for the same object with different words.