关键词: 情感分析, 多分类标注, 卷积神经网络, 深度学习

Abstract: Deep learning based approaches achieved less for sentiment classification with multiple labels. For this issue, this paper proposes a model called mwmpCNN（multi-windows and multi-pooling Convolutional Neural Network） to grasp the semantic distance and various emotional levels. mwmpCNN assemblies convolution layer with multiple windows to extract local context semantic, and then applies multi-pooling layer to keep multi-level semantic in short text when the feature dimension is reduced. Here, the text feature vector is constructed and reflected by the multi-level semantic, and connection layer is implemented for multi-label classification. This paper evaluates mwmpCNN by the test on Stanford Sentiment Treebank. mwmpCNN exhibits the classification accuracy of 54.6% and 43.5% respectively for the multi-label classification task.

Key words: sentiment analysis, multi-category classification, convolutional neural network, deep learning