Most of the existing Weibo sentiment analysis focuses on monolingual corpus like Chinese.However

a mixed use of Chinese and English becomes a popular form of expression.To better capture the social attention on public events

this paper proposes a bilingual lexicon based multi-class semantic orientation analysis for bilingual microblogs.We compare our proposed methodologies with majority vote

support vector machine (SVM) and K-nearest neighbor (KNN) by using cosine similarity which are competitive baseline methods.The experimental results show that our proposed methods outperform the three approaches we mentioned in terms of the accuracy and F1 score.