TY  - EJOU
AU  - Zhang, Yuhong  
AU  - Wang, Qinqin  
AU  - Li, Yuling  
AU  - Wu, Xindong  

TI  - Sentiment Classification Based on Piecewise Pooling Convolutional Neural Network
T2  - Computers, Materials \& Continua

PY  - 2018
VL  - 56
IS  - 2
SN  - 1546-2226

AB  - 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.
KW  - Sentiment classification
KW  -  convolutional neural network
KW  -  piecewise pooling
KW  -  feature extract

DO  - 10.3970/cmc.2018.02604