Abstract：Targets are usually discussed together. Sentiment towards the given target may be different from the sentiment polarity of the whole text. It is necessary to focus on the related context to the target in the whole semantic scenario for targeted sentiment analysis tasks. This paper presents a targeted sentiment classification method based on convolutional neural network(CNN) with Part-of-Speech(POS) and attention mechanism. POS information is introduced into the model as a supplement to text features. Attention mechanism with respect to the given target is built based on long short term memory neural network(LSTM) modeling of the input sequence. Then, the relevant parts to the target of the input text are enhanced according to the attention and the modified sequence is input to CNN sentiment classification structure to analyze the polarity towards the given target. POS information helps to capture the context with collocation relation to the target, which will help to reduce the influence of the context with similar content or short distance but no collocation relation. LSTM and CNN modeling the input text together can be beneficial to capture semantics of the whole text and those towards the given target at the same time effectively. Experiments on SemEval2014 dataset shows the effectiveness of the model compared to attention methods based on LSTM.
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2018, Vol. 31 
