Person-to-person evaluations are prevalent in all kinds of discourse and important for establishing reputations, building social bonds, and shaping public opinion. Such evaluations can be analyzed separately using signed social networks and textual sentiment analysis, but this misses the rich interactions between language and social context. To capture such interactions, we develop a model that predicts individual A's opinion of individual B by synthesizing information from the signed social network in which A and B are embedded with sentiment analysis of the evaluative texts relating A to B. We prove that this problem is NP-hard but can be relaxed to an efficiently solvable hinge-loss Markov random field, and we show that this implementation outperforms text-only and network-only versions in two very different datasets involving community-level decision-making: the Wikipedia Requests for Adminship corpus and the Convote U.S. Congressional speech corpus.

我们提出了一种模型，通过整合包括签名社交网络和情感分析在内的多种信息，来预测个体A对个体B的观点。我们证明了这是一个NP-hard问题，但可以通过一个高效可行的hinge-loss Markov随机场来实现，我们还展示了这个实现在两个不同的数据集上胜过仅使用文本或网络。

利用社交网络结构进行人际情感分析