Vertical and Sequential Sentiment Analysis of Micro-blog Topic

  • Shuo Wan
  • Bohan LiEmail author
  • Anman Zhang
  • Kai Wang
  • Xue Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11323)


Sentiment analysis of micro-blog topic aims to explore people’s attitudes towards a topic or event on social networks. Most existing research analyzed the micro-blog sentiment by traditional algorithms such as Naive Bayes and SVM based on the manually labelled data. They do not consider timeliness of data and inwardness of the topics. Meanwhile, few Chinese micro-blog sentiment analysis based on large-scale corpus is investigated. This paper focuses on the analysis of sequential sentiment based on a million-level Chinese micro-blog corpora to mine the features of sequential sentiment precisely. Distant supervised learning method based on micro-blog expressions and sentiment lexicon is proposed and fastText is used to train word vectors and classification model. The timeliness of analysis is guaranteed on the premise of ensuring the accuracy of classifier. The experiment shows that the accuracy of the classifier reaches 92.2%, and the sequential sentiment analysis based on this classifier can accurately reflect the emotional trend of micro-blog topics.


Vertical sentiment analysis fastText Distant supervision Sequential analysis 


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Shuo Wan
    • 1
  • Bohan Li
    • 1
    • 2
    • 3
    Email author
  • Anman Zhang
    • 1
  • Kai Wang
    • 1
  • Xue Li
    • 1
    • 4
  1. 1.College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.Collaborative Innovation Center of Novel Software Technology and IndustrializationNanjingChina
  3. 3.Jiangsu Easymap Geographic Information Technology Corp., Ltd.NanjingChina
  4. 4.School of Information Technology and Electrical EngineeringUniversity of QueenslandBrisbaneAustralia

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