Aspect-level sentiment analysis using context and aspect memory network

With the popularity of social networks, sentiment analysis has become one of the hottest topics in natural language processing (NLP). As the development of research on the fine-grained sentiment analysis, more and more researchers pay attention to aspect-level sentiment analysis. It aims to identify the same or different sentiment polarity in different aspects of the context. In this paper, a context and aspect memory network (CAMN) method is proposed to solve the problem of aspect level sentiment analysis. In this method, deep memory network, bi-directional long short-term memory network and multi-attention mechanism are introduced to better capture the sentiment features in short texts. It includes two strategies: one is to use the self-attention mechanism (i.e., CAMN-SA) to calculate the context relevance; the other is to use the encoder-decoder attention mechanism (i.e., CAMN-ED) to calculate the context and aspect relevance. In order to verify the function of each component in the proposed method, and to test the effect of different hops on the memory network, we conduct many experiments on three real-world datasets to compare the baseline models with our proposed method. Experimental results show that our proposed method can achieve better performance than the baseline models.