Advertisement

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)

Abstract

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.

Keywords

Vertical sentiment analysis fastText Distant supervision Sequential analysis 

References

  1. 1.
    Wang, S., Manning, C.D.: Baselines and bigrams: simple, good sentiment and topic classification. In: Meeting of the Association for Computational Linguistics: Short Papers, 08–14 July 2012, Jeju, Island, Korea, pp. 90–94. Association for Computational Linguistics (2012)Google Scholar
  2. 2.
    Joulin, A., Grave, E., Bojanowski, P., et al.: Bag of tricks for efficient text classification. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Short Papers, April 2017, pp. 427–431. Association for Computational Linguistics (2017)Google Scholar
  3. 3.
    Ku, L.W., Liang, Y.T., Chen, H.H.: Opinion extraction, summarization and tracking in news and blog corpora. In: AAAI-CAAW (2006)Google Scholar
  4. 4.
    Zhu, Y.L., Min, J., Zhou, Y., et al.: Semantic orientation computing based on HowNet. J. Chin. Inf. Process. 20(1), 14–20 (2006)Google Scholar
  5. 5.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: Sentiment classification using machine learning techniques. In: Proceedings of EMNLP, pp. 79–86. Association for Computational Linguistics (2002)Google Scholar
  6. 6.
    Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision. Cs224n Project Report (2009)Google Scholar
  7. 7.
    Yue, L., Chen, W., Li, X., Zuo, W., Yin, M.: A survey of sentiment analysis in social media. Knowl. Inf. Syst. 1–47 (2018)Google Scholar
  8. 8.
    Park, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: International Conference on Language Resources and Evaluation, LREC 2010, 17–23 May 2010, Valletta, Malta. DBLP (2010)Google Scholar
  9. 9.
    Iosifidis, V., Ntoutsi, E.: Large scale sentiment learning with limited labels. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1823–1832. ACM (2017)Google Scholar
  10. 10.
    Mikolov, T., Chen, K., Corrado, G., et al.: Efficient estimation of word representations in vector space. Comput. Sci. (2013)Google Scholar
  11. 11.
    Bojanowski, P., Grave, E., Joulin, A., et al.: Enriching word vectors with subword information (2016)Google Scholar
  12. 12.
    Culotta, A.: Towards detecting influenza epidemics by analyzing Twitter messages. In: Proceedings of the First Workshop on Social Media Analytics, Washington, DC, Columbia, 25–28 July 2010, pp. 115–122. ACM (2010)Google Scholar
  13. 13.
    Nahar, V., Al-Maskari, S., Li, X., Pang, C.: Semi-supervised learning for cyberbullying detection in social networks. In: Wang, H., Sharaf, M.A. (eds.) ADC 2014. LNCS, vol. 8506, pp. 160–171. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-08608-8_14CrossRefGoogle Scholar
  14. 14.
    Paul, D., Li, F., Teja, M.K., et al.: Spatio temporal sentiment analysis of US Election what Twitter says!. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1585–1594. ACM (2017)Google Scholar
  15. 15.
    Du, J.F.: Weibo arguing laws: a sketch of social psychology on the Internet. News Writ. (06), 65 (2017)Google Scholar
  16. 16.
    Xie, L., Zhou, M., Sun, M.-S.: Hierarchical structure based hybrid approach to sentiment analysis of Chinese micro blog and its feature extraction. J. Chin. Inf. Process. 26(1), 73–83 (2012)Google Scholar

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

Personalised recommendations