中文摘要：

微博日益成为一个巨大而复杂的互联网舆论平台。分析微博中特定话题的情感趋势对于了解网络舆情、分析产品销量趋势显得尤为重要。该文使用微博进行真实事件公众情感趋势预测：首先,考虑到微博特征稀疏、上下文缺失的特性,借助词语上下位语义关系对其进行语义扩充;其次,使用语义特征和情感常识知识构造双层分类方法进行情感分析;最后,对特定事件在连续时间段内的微博使用时序情感分析方法进行公众情感趋势预测。实验证明,该情感分析方法准确率相对于传统分类方法有明显的提高,在此基础上的情感趋势预测符合事件的真实发展状况。

英文摘要：

Microblog is a large and complicated public opinion platform on the Internet. In this paper, we demonstrate how microblogs can be used to predict real world public sentiment trends of events. Firstly, considering the special properties of mieroblogs, absence of context and sparseness of feature, we use the hyponymy relationship between words to do semantic extension for each microblog. Secondly, with the help of semantic feature and affective commonsense knowledge, we can decide the sentiment of each microblog through constructing a double-layer text classifier. Finally, public sentiment trend prediction of each event is performed by using time series sentiment analysis of microblogs. The experiment results show that our sentiment analysis method has a better performance than state--of--the art classification methods. Besides, the sentiment trends of events are consistent with the development of the real world situation to a large degree.