关键词: 卷积神经网络, 注意力, 情感分类, 多通道

Abstract: In order to solve the problem of emotional ambiguity caused by the change of contextual order in traditional neural networks. A multi-channel semantic synthesis convolution neural network（SFCNN） is proposed. Firstly, the improved emotional attention mechanism focuses on the emotional weighting of word vectors. Secondly, the emotional tendency word vector is fused by multi-channel semantic synthesis layer to generate deep semantic vectors with text context semantic information, and construct sentiment classification model. Finally, the gradient descent algorithm for adaptive learning rate is used to optimize the model parameter, and to complete emotion classification task. Multiple Weibo data sets are used to verify the effectiveness of the proposed algorithm. The experimental results show that the improved affective tendency attention mechanism with the multi-channel semantic fusion CNN has better emotion classification ability, and the gradient descent algorithm of adaptive learning rate can complete the convergence of the model faster.

Key words: Convolution Neural Network（CNN）, attention, emotional classification, multi-channel