Tb-CNN: Joint tree-bank information for sentiment analysis using CNN

Sentiment analysis still is a problem in text data analysis. In this paper, the proposed model which fuses the convolution neural network with the tree bank information has been introduced. This model not only takes into consideration of the syntax information, but also the structure information which is better than other single factor model. By a sequence of experiments, we can prove the goodness of this model in sentiment analysis.

[1]  Noah A. Smith,et al.  Learning Word Representations with Hierarchical Sparse Coding , 2014, ICML.

[2]  Cícero Nogueira dos Santos,et al.  Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts , 2014, COLING.

[3]  Yoon Kim,et al.  Convolutional Neural Networks for Sentence Classification , 2014, EMNLP.

[4]  Matthew D. Zeiler.  ADADELTA: An Adaptive Learning Rate Method , 2012, ArXiv.

[5]  Jeffrey Pennington,et al.  GloVe: Global Vectors for Word Representation , 2014, EMNLP.

[6]  Bo Xu,et al.  Parallel Recursive Deep Model for Sentiment Analysis , 2015, PAKDD.

[7]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[8]  Jason Weston,et al.  Natural Language Processing (Almost) from Scratch , 2011, J. Mach. Learn. Res..

[9]  Gjorgji Madjarov,et al.  Twitter Sentiment Analysis Using Deep Convolutional Neural Network , 2015, HAIS.

[10]  Andrea Kleinsmith,et al.  Affective Body Expression Perception and Recognition: A Survey , 2013, IEEE Transactions on Affective Computing.

[11]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[12]  Xiang Zhang,et al.  Character-level Convolutional Networks for Text Classification , 2015, NIPS.

[13]  Darren Burke,et al.  Judging approachability on the face of it: the influence of face and body expressions on the perception of approachability. , 2011, Emotion.

[14]  Christopher Potts,et al.  Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank , 2013, EMNLP.