Abstract: Microblog is a social platform that people can share life,express opinions and vent emotions.Due to the large amount of data and easy access,the Microblog data has been widely used in emotion prediction for the web users.The traditional research on emotion classification of Microblog simply stays on the meaning of words,without considering the influence from the individuation of each person's language preference and style,which results a lower accuracy of the emotion classification.Firstly,this paper constructs a user interest dictionary by analyzing user interest characteristics and proposes a user interest dictionary basedemotion classification model.Secondly,by using the advantage of high classification accuracy of Long Short-Term Memory (LSTM),this paper trains a common LSTM based classification model.Finally,this paper fuses different models by using Support Vector Machine to obtain the final emotion classification results.The experimental results show that,compared with traditional classifiers such as SVM and Naive Bayesian,the personalized emotion classification method based on user interest dictionary and LSTM has a great improvement on classification accuracy.Compared with typical deep learning methods like LSTM andRecurrent Neural Network,the proposed method can obtain higher classification accuracy while ensuring the execution efficiency.

Key words: Emotion classification, LSTM model, Support vector machine, User interest dictionary