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Discovering Sentiment of Social Messages by Mining Message Correlations

  • Hsin-Chang Yang
  • Chung-Hong Lee
  • Chun-Yen Wu
  • Yu-Chian Huang
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 297)

Abstract

With explosive growth of the Internet, the amount of information in text form is growing rapidly and the demand for data analysis is also increases. We can perform sentiment analysis on a large set of text messages to discover valuable knowledge and obtain enormous benefits in national security, business, politics, economics, etc. However, text messages from the social networks are rather different from those of traditional text documents. Therefore, it is difficult but essential to develop an effective method of sentiment exploration in social networks. In this paper we first applied a neural network model, namely the self-organizing maps, to cluster similar messages and sentiment keywords, respectively. We then developed an association discovery process to find the associations between a message and some sentiment keywords. The sentiment of a message is then determined according to such associations. We performed experiments on Twitter messages and obtained promising results.

Keywords

Sentiment Analysis Social Network Analysis Text Mining Self-Organizing Map 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Hsin-Chang Yang
    • 1
  • Chung-Hong Lee
    • 2
  • Chun-Yen Wu
    • 3
  • Yu-Chian Huang
    • 3
  1. 1.Dept. Information ManagementNational University of KaohsiungKaohsiungTaiwan
  2. 2.Dept. Electrical EngineeringNational Kaohsiung University of Applied SciencesKaohsiungTaiwan
  3. 3.Institute of Information ManagementNational University of KaohsiungKaohsiungTaiwan

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