With the rise of microblogging services like Twitter and Sina Weibo, users are able to post various contents on breaking news, public events, or products conveniently and swiftly. These massive contents carry users’ mass sentiment and opinions on various topics, which are a kind of useful and timely source. Traditional microblog sentiment analysis methods often assume that microblogs are independent and identically distributed, they ignore the fact that the microblogs are networked data. Although some methods take the relations between microblogs into consideration, they only use shallow network features which are not sufficient, such as neighbors. Besides, these methods are content-based methods because they cannot use social context information in the prediction stage. To solve this problem, in this paper we use a deep learning method to fully capture the features of microblog relations including both the implicit and explicit ones and use these features to promote microblog sentiment analysis results. Specifically, we first construct a graph which models the relations between microblogs inspired by sentiment consistency and emotional contagion theories. Then we embed the microblog graph and get a continuous vector representation for social contexts of each microblog. After that, we propose a novel neural network to integrate social context knowledge with text information. To handle the problem that different words have different contributions to the classification result, we introduce the attention mechanism into our model. We conduct experiments on three publicly released datasets. The experimental results show that our proposed model can outperform state-of-the-art methods consistently and significantly.

随着微博服务（如Twitter和新浪微博）的兴起，用户可以方便快捷地在突发新闻，公共事件或产品上发布各种内容。这些海量内容承载着用户对各种主题的广泛情感和见解，是一种有用且及时的信息来源。传统的微博情感分析方法通常假设微博是独立且分布均匀的，而忽略了微博是网络数据的事实。尽管某些方法考虑了微博客之间的关系，但它们仅使用了不足的浅层网络功能（例如邻居）。此外，这些方法是基于内容的方法，因为它们无法在预测阶段使用社交上下文信息。为了解决这个问题，在本文中，我们使用深度学习方法来充分捕捉微博关系的特征，包括隐性和显性关系，并利用这些特征来促进微博情感分析结果。具体而言，我们首先构建一个图表，该图表可模拟受情感一致性和情感传染理论启发的微博之间的关系。然后，我们嵌入微博图，并获得每个微博社交环境的连续向量表示。之后，我们提出了一种新颖的神经网络，将社交环境知识与文本信息整合在一起。为了解决不同单词对分类结果的贡献不同的问题，我们在模型中引入了注意力机制。我们对三个公开发布的数据集进行了实验。