In online social networks people often express attitudes towards others,
which forms massive sentiment links among users. Predicting the sign of
sentiment links is a fundamental task in many areas such as personal
advertising and public opinion analysis. Previous works mainly focus on textual
sentiment classification, however, text information can only disclose the "tip
of the iceberg" about users' true opinions, of which the most are unobserved
but implied by other sources of information such as social relation and users'
profile. To address this problem, in this paper we investigate how to predict
possibly existing sentiment links in the presence of heterogeneous information.
First, due to the lack of explicit sentiment links in mainstream social
networks, we establish a labeled heterogeneous sentiment dataset which consists
of users' sentiment relation, social relation and profile knowledge by
entity-level sentiment extraction method. Then we propose a novel and flexible
end-to-end Signed Heterogeneous Information Network Embedding (SHINE) framework
to extract users' latent representations from heterogeneous networks and
predict the sign of unobserved sentiment links. SHINE utilizes multiple deep
autoencoders to map each user into a low-dimension feature space while
preserving the network structure. We demonstrate the superiority of SHINE over
state-of-the-art baselines on link prediction and node recommendation in two
real-world datasets. The experimental results also prove the efficacy of SHINE
in cold start scenario.

本文研究在线社交网络中的情感链接识别问题，提出了一种基于异构网络嵌入的新型框架 SHINE，能够从与用户有关的文本、社交关系以及用户个人信息多方面进行分析，从而预测隐藏在社交网络中的情感关系。实验结果表明，所提出的方法在网络关系预测及节点推荐方面优于当前的先进算法，并且也能够很好地应用于「零样本」情况。