Emotion analysis of microblog based on emotion dictionary and Bi-GRU

The colloquialism and conciseness of microblog text bring additional challenges to emotion classification. This paper proposes a new emotion classification model based on hybrid learning. In the first stage, the improved dictionary classification method is used to calculate emotion score in the whole data set, and the data with high or low scores are directly marked; in the second stage, the rest of the method is based on emotion dictionary and Bi-GRU fusion model to calculate emotion score, and the two stage hybrid frame makes the method effectively applied in microblog emotion classification. The microblog experiment with emoticons in COAE2014 (Chinese opinion analysis and evaluation 2014) dataset shows that the single model is not ideal for many kinds of complex contexts, and it is difficult and low accuracy. The multi model fusion method can effectively improve the error preference of the single model and improve the classification effect.