Topic-Based Microblog Polarity Classification Based on Cascaded Model

  • Quanchao Liu
  • Yue Hu
  • Yangfan Lei
  • Xiangpeng Wei
  • Guangyong Liu
  • Wei Bi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10861)


Given a microblog post and a topic, it is an important task to judge the sentiment towards that topic: positive or negative, and has important theoretical and application value in the public opinion analysis, personalized recommendation, product comparison analysis, prevention of terrorist attacks, etc. Because of the short and irregular messages as well as containing multifarious features such as emoticons, and sentiment of a microblog post is closely related to its topic, most existing approaches cannot perfectly achieve cooperating analysis of topic and sentiment of messages, and even cannot know what factors actually determined the sentiment towards that topic. To address the issues, MB-LDA model and attention network are applied to Bi-RNN for topic-based microblog polarity classification. Our cascaded model has three distinctive characteristics: (i) a strong relationship between topic and its sentiment is considered; (ii) the factors that affect the topic’s sentiment are identified, and the degree of influence of each factor can be calculated; (iii) the synchronized detection of the topic and its sentiment in microblog is achieved. Extensive experiments show that our cascaded model outperforms state-of-the-art unsupervised approach JST and supervised approach SSA-ST significantly in terms of sentiment classification accuracy and F1-Measure.


Cascaded model Attention model LDA model Bi-RNN Sentiment analysis Microblog topic 



This paper is financially supported by The National Key Research and Development Program of China (No. 2017YFB0803003) and National Science Foundation for Young Scientists of China (No. 6170060558). We would like to thank the anonymous reviewers for many valuable comments and helpful suggestions. Our future work will be carried out in the following aspects: firstly, the file attribute information of microblog users is incorporated into microblog message emotional polarity and thematic reasoning in order to improve the accuracy of polarity classification; Secondly, more explicit emotional features are excavated into the attention network to improve the accuracy of the polarity classification.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Quanchao Liu
    • 1
    • 2
  • Yue Hu
    • 1
    • 2
  • Yangfan Lei
    • 2
  • Xiangpeng Wei
    • 2
  • Guangyong Liu
    • 4
  • Wei Bi
    • 3
  1. 1.Institute of Information EngineeringChinese Academy of ScienceBeijingChina
  2. 2.University of Chinese Academy of ScienceBeijingChina
  3. 3.SeeleTech CorporationSan FranciscoUSA
  4. 4.BeijingChina

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