Sentiment Classification Based on Piecewise Pooling Convolutional Neural Network

• 作者: Qinqin Wang Xindong Wu Yuhong Zhang Yuling Li
• 来源期刊: 计算机、材料和连续体(英文)
• 学科: 工学
• 关键词: SENTIMENT classification convolutional NEURAL NETWORK PIECEWISE pooling FEATURE EXTRACT
• 年，卷（期）: 2018,（8）
• 页码范围: 285-297
• 页数: 13页
• 分类号: TP3

摘要

Recently,the effectiveness of neural networks,especially convolutional neural networks,has been validated in the field of natural language processing,in which,sentiment classification for online reviews is an important and challenging task.Existing convolutional neural networks extract important features of sentences without local features or the feature sequence.Thus,these models do not perform well,especially for transition sentences.To this end,we propose a Piecewise Pooling Convolutional Neural Network(PPCNN)for sentiment classification.Firstly,with a sentence presented by word vectors,convolution operation is introduced to obtain the convolution feature map vectors.Secondly,these vectors are segmented according to the positions of transition words in sentences.Thirdly,the most significant feature of each local segment is extracted using max pooling mechanism,and then the different aspects of features can be extracted.Specifically,the relative sequence of these features is preserved.Finally,after processed by the dropout algorithm,the softmax classifier is trained for sentiment classification.Experimental results show that the proposed method PPCNN is effective and superior to other baseline methods,especially for datasets with transition sentences.