People often express opinions towards others in a social network, causing sentiment links to form among users. To develop effective methods for discovering implicit sentiment links among users, the extraction and exploitation of structural semantic information from heterogeneous social networks are of great importance. We propose a novel heterogeneous social network embedding-based approach for sentiment link prediction that takes both global structural information with multi-dimensional relations and heterogeneous context information into consideration to leverage rich and intrinsic association information. Specifically, the attributed heterogeneous social network and Sentic LSTM-based sentiment link network are employed to incorporate various explicit context knowledge and implicit multi-dimensional user interaction association knowledge into representation learning and sentiment link prediction. The experimental results on a real-world dataset show that the proposed approach has advantages over the state-of-the-art baselines. The results show the effectiveness of incorporating social relations and profile context information into sentiment link prediction, especially in cold-start scenarios. The learned embedding representation that incorporates both structural information with multi-dimensional relations and context information from heterogeneous social networks can improve sentiment link prediction performance. The proposed approach is effective and feasible for detecting unobserved sentiment links from online social networks and outperforms the state-of-the-art baselines in sentiment link prediction tasks.

人们经常在社交网络中对他人表达意见，从而在用户之间形成情感链接。为了开发有效的方法来发现用户之间的隐式情感联系，从异构社交网络中提取和利用结构语义信息非常重要。我们提出了一种新颖的基于异构社交网络嵌入的情感链接预测方法，该方法将具有多维关系的全局结构信息和异构上下文信息都考虑在内，以利用丰富的内在关联信息。特别，归因的异构社交网络和基于Sentic LSTM的情感链接网络被用于将各种显式上下文知识和隐式多维用户交互关联知识纳入表示学习和情感链接预测。在真实数据集上的实验结果表明，所提出的方法具有优于最新基准的优点。结果表明，将社会关系和个人资料上下文信息纳入情感链接预测的有效性，尤其是在冷启动场景中。结合了具有多维关系的结构信息和来自异构社交网络的上下文信息的学习型嵌入表示可以提高情感链接预测性能。