Aspect-words Sentiment analysis of commodity comments based on deep memory network

The social model is a huge virtual platform where to freely express themselves and give their views and feelings, influencing any aspect of life, with implications for marketing and communication alike. Aspect words sentiment analysis can more accurately understand user needs and improve enterprise marketing strategies. In current researches on aspect words sentiment analysis, researchers use the integration of attention mechanism and LSTM to obtain key information. However, there are few studies on the fusion of aspect words, context, and multi-layer deep memory networks. Therefore, we proposed a multi-layer deep memory network model based on the splicing of aspect terms and context vectors. The model can further strengthen the fusion between aspect words and context vectors, and make up for the shortcomings of LSTM in transmitting information loss. Experimental results on Restaurant and Laptop datasets show that the proposed method has a better performance.