Sentiment analysis which aims to predict users’ opinions is a huge need for many industrial services. In recent years, many methods based on neural network achieve great performance on sentiment analysis such as Tang et al. (2014); Socher et al. (2013); Mikolov et al. (2013b). However, most of existing methods only focus on local documents and do not consider related user profile and product information (Kim, 2014; Yang et al., 2016; Long et al., 2017). Even some works attempt to combine those background information in sentiment analysis, they normally treat user profile and product information as a united entity and ignore that different background information may cause different aspects of influences to users’ sentiments(Tang et al., 2015b; Chen et al., 2016; Dou, 2017). To address these issues, we proposed a new Joint User and Product Memory Network (JUPMN) utilizing user profile and product information in separate ways into sentiment classification. Inspired by the successful utilization of memory network (Weston et al., 2014; Sukhbaatar et al., 2015), our model first creates document representations using hierarchical LSTM model and then feeds the document vectors into new carefully designed user and product memory networks to reflect corresponding features. The evaluation of JUPMN on three benchmark review datasets IMDB, Yelp13 and Yelp14 shows that JUPMN outperforms the state-of-the-art model and further analysis of experimental results is employed.
Bachelor Thesis (Capstone Project Report),