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In sentiment analysis (SA) of product reviews, both user and product information are proven to be useful. Current tasks handle user pro- file and product information in a unified model which may not be able to learn salient fea- tures of users and products effectively. In this work, we propose a dual user and product memory network (DUPMN) model to learn user profiles and product reviews using sepa- rate memory networks. Then, the two repre- sentations are used jointly for sentiment pre- diction. The use of separate models aims to capture user profiles and product information more effectively. Compared to state-of-the- art unified prediction models, the evaluations on three benchmark datasets, IMDB, Yelp13, and Yelp14, show that our dual learning model gives performance gain of 0.6%, 1.2%, and 0.9%, respectively. The improvements are also deemed very significant measured by p-values.
In EMNLP 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, 2018

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), 2018

In this paper, we propose BlocHIE, a Blockchain-based platform for healthcare information exchange. First, we analyze the different requirements for sharing healthcare data from different sources. Based on the analysis, we employ two loosely-coupled Blockchains to handle different kinds of healthcare data. Second, we combine off-chain storage and on-chain verification to satisfy the requirements of both privacy and authenticability. Third, we propose two fairness-based packing algorithms to improve the system throughput and the fairness among users jointly. To demonstrate the practicability and effectiveness of BlocHIE, we implement BlocHIE in a minimal-viable-product way and evaluate the proposed packing algorithms extensively.
In Proceedings of the 4th IEEE International Conference on Smart Computing (SMARTCOMP), 2018