Learning good semantic vector representations for sentiment analysis in phrases, sentences
and paragraphs is a challenging and ongoing area of natural language processing. In this
paper, we propose a Convolution Neural Network for aspect level sentiment classification.
Our model first builds a convolution neural network model to aspect extraction. Afterwards,
we used a sequence labeling approach with Conditional Random Fields for the opinion
target detection. Finally, we concatenate an aspect vector with every word embedding and …

Aspect-based sentiment analysis has received considerable attention in recent years
because it can provide more detailed and specific user opinion information. Most existing
methods based on recurrent neural networks usually suffer from two drawbacks: information
loss for long sequences and a high time consumption. To address such issues, a hybrid
attention model is proposed for aspect-based sentiment analysis in this paper, which utilizes
only attention mechanisms rather than recurrent or convolutional structures. In this model, a …