Aspect and Sentiment Unification Model for Twitter Analysis

With the special "@, #, //" symbols, which include a lot of emotional symbols and pictures etc., tweets are different with other user-generated general texts, such as blogs, forums, reviews. Considering structural features and content of tweets, we present a semi-supervised Aspect and Sentiment Unification ModelPL-SASU. Using more information rather than solo texts, this model can model tweets better. The experiments of sentiment classification and aspect identification on real twitter data show that PL-SASU outperforms JTS, ASUM and UTSU model.

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