Aspect and Sentiment Unification Model for Twitter Analysis

  • Hui Zhang
  • Tong-xin Wang
  • Yi-qun Liu
  • Shao-ping Ma
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9243)


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 Model(PL-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.


Tweet Sentiment classification Aspect identification Aspect and sentiment unification model Emotional symbol 



This work was supported by National Key Basic Research Program (2015CB358700) and Natural Science Foundation (61472206, 61073071) of China.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Hui Zhang
    • 1
    • 2
  • Tong-xin Wang
    • 3
  • Yi-qun Liu
    • 1
  • Shao-ping Ma
    • 1
  1. 1.State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory of Information Science and Technology, Department of Computer Science and TechnologyTsinghua UniversityBeijingChina
  2. 2.Operation Experiment CenterNanjing Army Command CollegeNanjingChina
  3. 3.Department of PhysicsFudan UniversityShanghaiChina

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