关键词: 情感分类, 卷积神经网络, 产品词向量, 加权池化层, dropout算法

Abstract: Combining the commentary information of different products to construct intelligent sentiment classifier, this paper proposes an online product commentary sentiment analysis model combining product features：PWCNN（Product Weight Convolution Neural Network）. The model firstly combines word vectors of product word features by vector multiplication, and the result is taken as input of the model. Then, according to the importance of sentences, a pooled weighted convolutional neural network is used to learn the document-level representation of the reviews. In order to prevent over-fitting and improve the generalization ability, a dropout strategy is adopted at the output layer. The experimental results show that the PWCNN model achieves the best results in terms of average accuracy and [F1] value, and the training speed is improved.

Key words: sentiment classification, convolutional neural network, product word vector, weighted pooling layer, dropout algorithm