Microblog sentiment analysis has become a hot research area due to its wide applications. There are some methods utilizing social context, but they only built a global sentiment analysis model, failing to extract personalized expressions. Some personalized methods have been proposed to deal with this problem, but they suffer from data sparseness and inefficiency. Based on personalized sentiment analysis methods, we exploit social context information and capture users’ variable and distinctive expressions at a community level to handle these problems. In particular, we propose a collaborative microblog sentiment analysis approach. In our approach, two classifiers are constructed. One is the global microblog sentiment analysis model which can exploit the sentiment shared by all users. One is the community-specific microblog sentiment analysis model which can extract sentiment influenced by user personalities. In addition, we extract community similarity knowledge and employ it to improve the learning process of the community-specific sentiment model. Moreover, we incorporate social contexts into this model as regularization to encourage the sharing sentiment between connected microblogs. An accelerated algorithm is introduced to solve our model. Experiments on two real datasets show that our model can advance the performance of microblog sentiment classification effectively and outperform state-of-art methods significantly.

微博情感分析由于其广泛的应用而成为研究的热点。有一些利用社交环境的方法，但它们仅建立了全球情感分析模型，无法提取个性化表达。已经提出了一些个性化方法来解决该问题，但是它们遭受数据稀疏和低效的困扰。基于个性化的情感分析方法，我们利用社交环境信息并在社区级别捕获用户的变量和独特表达来处理这些问题。特别是，我们提出了一种协作微博情感分析方法。在我们的方法中，构造了两个分类器。一种是全球微博情感分析模型，它可以利用所有用户共享的情感。一种是特定于社区的微博情感分析模型，该模型可以提取受用户个性影响的情感。此外，我们提取社区相似性知识，并将其用于改善特定于社区的情感模型的学习过程。此外，我们将社交情境作为正则化方法纳入该模型中，以鼓励相关微博之间的共享情绪。引入了一种加速算法来求解我们的模型。在两个真实数据集上进行的实验表明，我们的模型可以有效地提高微博情感分类的性能，并且明显优于最新方法。此外，我们将社交情境作为正则化方法纳入该模型中，以鼓励相关微博之间的共享情绪。引入了一种加速算法来求解我们的模型。在两个真实数据集上进行的实验表明，我们的模型可以有效地提高微博情感分类的性能，并且明显优于最新方法。此外，我们将社交情境作为正则化方法纳入该模型中，以鼓励相关微博之间的共享情绪。引入了一种加速算法来求解我们的模型。在两个真实数据集上进行的实验表明，我们的模型可以有效地提高微博情感分类的性能，并且明显优于最新方法。