A hybrid model of sentimental entity recognition on mobile social media

With new forms of media such as Twitter becoming increasingly popular, the Internet is now the main conduit of individual and interpersonal messages. A considerable amount of people express their personal opinions about news-related subject through Twitter, a popular SNS platform based on human relationships. It provides us a data source that we can use to extract peoples' opinions which are important for product review and public opinion monitoring. In this paper, a hybrid sentimental entity recognition model (HSERM) has been designed. Utilizing 100 million collected messages from Twitter, the hashtag is regarded as the label for sentimental classification. In the meanwhile, features as emoji and N-grams have been extracted and classified the collected topic messages into four different sentiment categories based on the circumplex sentimental model. Finally, machine learning methods are used to classify the sentimental data set, and an 89 % precise result has been achieved. Further, entities that are behind emotions could be gotten with the help of SENNA deep learning model.