Application of Machine Learning Techniques to Tweet Polarity Classification with News Topic Analysis

  • Authors

    • Hoyeon Park
    • Hyeonjeong Seo
    • Kyoung jae Kim
    • Gundoo Moon
    2018-09-15
    https://doi.org/10.14419/ijet.v7i4.4.19606
  • Polarity classification, Topic analysis, Machine learning.

  • Abstract

    The exponential growth of online community provides the tremendous amount of textual information in terms of human behavioral reaction. Thus, online social media platforms such as Twitters, Facebook and YouTube are reflected as an essential part of human relationship networks. Especially, Twitter is widely applied to the disaster situation as a text and it provides critical insights into emergency management. In this study, we propose a topic analysis and sentiment polarity classification with machine learning techniques for emergency management. In this study, we compared the polarity classification models using three machine learning methods and found that the model with random forests showed the best classification performance.

     

     

  • References

    1. [1] F. A. Pozzi, E. Fersini, E. Messina, & B. Liu, Sentiment Analysis in Social Networks, Morgan Kaufmann, 2016.

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      [3] J. Bollen, H. Mao, & X. Zeng, Twitter mood predicts the stock market, Journal of Computational Science. 2 (2011), 1-8.

      [4] B. Liu, Y. Dai, X. Li, W. S. Lee, & P. S. Yu, Building text classifiers using positive and unlabeled examples, Proceedings of Third IEEE International Conference on Data Mining. (2003), 179–186.

      [5] B. O'Connor, R. Balasubramanyan, B. R. Routledge, & N. A. Smith, From tweets to polls: Linking text sentiment to public opinion time series. Proceedings of the Fourth International Conference on Weblogs and Social Media, (2010), 122–129.

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  • How to Cite

    Park, H., Seo, H., jae Kim, K., & Moon, G. (2018). Application of Machine Learning Techniques to Tweet Polarity Classification with News Topic Analysis. International Journal of Engineering & Technology, 7(4.4), 40-41. https://doi.org/10.14419/ijet.v7i4.4.19606

    Received date: 2018-09-13

    Accepted date: 2018-09-13

    Published date: 2018-09-15