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Microblogging provides a large volume of text for learning and understanding people’s sentiments on a variety of topics. Much of the current work on sentiment analysis of microblogs (eg, tweets) focuses on document level polarity. However, identifying sentiment clues with respect to specific targets (eg, named entities) can be more useful than pure document polarity results. For example, sentiment clues such as “must see”,“awesome”,“rate 5 stars”(in the movie domain) are much more meaningful than the polarities of tweets only. Previous attempts at single-word sentiment clue extraction from formal text will not suffice for extracting multi-word sentiment phrases. Single words “must” and “see” do not separately convey polarity, but their combination “must see” expresses strong positive sentiment towards a movie target. Another issue with identifying sentiment clues is identifying informal sentiment expressions, such …