A Survey of Sentiment Analysis Based on Transfer Learning <s> VOLUME 7, 2019 <s> Convolutional neural network (CNN) is a neural network that can make use of the internal structure of data such as the 2D structure of image data. This paper studies CNN on text categorization to exploit the 1D structure (namely, word order) of text data for accurate prediction. Instead of using low-dimensional word vectors as input as is often done, we directly apply CNN to high-dimensional text data, which leads to directly learning embedding of small text regions for use in classification. In addition to a straightforward adaptation of CNN from image to text, a simple but new variation which employs bag-of-word conversion in the convolution layer is proposed. An extension to combine multiple convolution layers is also explored for higher accuracy. The experiments demonstrate the effectiveness of our approach in comparison with state-of-the-art methods. <s> BIB001 </s> A Survey of Sentiment Analysis Based on Transfer Learning <s> VOLUME 7, 2019 <s> The dominant approach for many NLP tasks are recurrent neural networks, in particular LSTMs, and convolutional neural networks. However, these architectures are rather shallow in comparison to the deep convolutional networks which have pushed the state-of-the-art in computer vision. We present a new architecture (VDCNN) for text processing which operates directly at the character level and uses only small convolutions and pooling operations. We are able to show that the performance of this model increases with depth: using up to 29 convolutional layers, we report improvements over the state-of-the-art on several public text classification tasks. To the best of our knowledge, this is the first time that very deep convolutional nets have been applied to text processing. <s> BIB002
Conneau et al. BIB002 applied the deeper CNN to NLP and proposed a new architecture VDCNN for text processing. The model combines VGGNet (Visual Geometry Group) with deep residual network (ResNet). The authors proved that using 29 convolutional layers can achieve better. Johnson and Zhang BIB001 used the one-dimensional structure of data for text classification and prediction. This method changed the practice of using low-dimensional word vectors as input, directly using high-dimensional text as the processing object of CNN, and also explored the expansion of combining multiple convolutional layers to get more accurate prediction. CNN can model the combination of features when processing text, and it is fast in processing speed, but it also has shortcomings. In order to achieve better results, new features need to be added to the CNN model.