Short texts such as tweets and E-commerce reviews can reflect people’s opinions on interested events or products, which are much beneficial to many applications. However, one opinion word may have different sentiment polarities when modifying different targets. Therefore, in this paper we propose to extract “
” that are represented by tuples of (
), indicating an opinion word and the target modified by the word. By extracting appraisal expressions, we can further construct target-sensitive sentiment dictionaries and improve the effectiveness of sentiment analysis on short texts. Consequently, we propose a
framework to extract appraisal expressions from short texts. In the
step, we extract appraisal-expression candidates, and in the
step, we use SVM to extract appraisal expressions and present a
approach to automatically label training data. Comparative experiments between our proposal and three baseline methods suggest the superiority and effectiveness of our proposal.