README.md

SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction

author:Hongwei Wang,Fuzheng Zhang,Miu Hou,Xing Xie,Minyi Guo,Qi Liu

abstract:In online social networks people often express attitudes towardsothers, which forms massive sentiment links among users. Predict-ing the sign of sentiment links is a fundamental task in many areassuch as personal advertising and public opinion analysis. Previousworks mainly focus on textual sentiment classication, however,text information can only disclose the “tip of the iceberg” aboutusers’ true opinions, of which the most are unobserved but impliedby other sources of information such as social relation and users’prole. To address this problem, in this paper we investigate howto predict possibly existing sentiment links in the presence of het-erogeneous information. First, due to the lack of explicit sentimentlinks in mainstream social networks, we establish a labeled het-erogeneous sentiment dataset which consists of users’ sentimentrelation, social relation and prole knowledge by entity-level sen-timent extraction method. Then we propose a novel and exibleend-to-end Signed Heterogeneous Information Network Embedding(SHINE) framework to extract users’ latent representations fromheterogeneous networks and predict the sign of unobserved sen-timent links. SHINE utilizes multiple deep autoencoders to mapeach user into a low-dimension feature space while preserving thenetwork structure. We demonstrate the superiority of SHINE overstate-of-the-art baselines on link prediction and node recommen-dation in two real-world datasets. The experimental results alsoprove the ecacy of SHINE in cold start scenario.

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curator:Ranran Chu