In view of the current emotional classification models for text on weibo platform

most of them conduct emotional analysis on the part of speech or emoji of a setence

etc. without understanding users ’ emotional tendency and lack of semantic understanding. Proposing a method of emotional classification for weibo users by using Word2 Vec combined with deep learning. Specifically

skip-gram model in Word2 Vec and negative sampling are used to train word vectors. Then bidirectional long short-term memory（ Bi LSTM）-attention mechanism（ ATT）model is used to automatically learn the emotional information in word vectors

capture the most representative features in text data

and finally classify the emotional tendencies of Weibo users through softmax layer. Test on NLPCC2013 data set

at the same time

made five group of contrast test. The results show that the new model AVP reaches 0. 814

AVF1 reaches 0. 831

and the effect is best when the dimension of word vector is 150.