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FCE-SVM: a new cluster based ensemble method for opinion mining from social media

Author

Listed:
  • Gang Wang

    (Hefei University of Technology
    Ministry of Education
    City University of Hong Kong)

  • Daqing Zheng

    (SUFE
    SUFE)

  • Shanlin Yang

    (Hefei University of Technology
    Ministry of Education)

  • Jian Ma

    (City University of Hong Kong)

Abstract

Opinion mining aiming to automatically detect subjective information has raised more and more interests from both academic and industry fields in recent years. In order to enhance the performance of opinion mining, some ensemble methods have been investigated and proven to be effective theoretically and empirically. However, cluster based ensemble method is paid less attention to in the area of opinion mining. In this paper, a new cluster based ensemble method, FCE-SVM, is proposed for opinion mining from social media. Based on the philosophy of divide and conquer, FCE-SVM uses fuzzy clustering module to generate different training sub datasets in the first stage. Then, base learners are trained based on different training datasets in the second stage. Finally, fusion module is employed to combine the results of based learners. Moreover, the multi-domain opinion datasets were investigated to verify the effectiveness of proposed method. Empirical results reveal that FCE-SVM gets the best performance through reducing bias and variance simultaneously. These results illustrate that FCE-SVM can be used as a viable method for opinion mining.

Suggested Citation

  • Gang Wang & Daqing Zheng & Shanlin Yang & Jian Ma, 2018. "FCE-SVM: a new cluster based ensemble method for opinion mining from social media," Information Systems and e-Business Management, Springer, vol. 16(4), pages 721-742, November.
    • Handle: RePEc:spr:infsem:v:16:y:2018:i:4:d:10.1007_s10257-017-0352-0
    DOI: 10.1007/s10257-017-0352-0
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    References listed on IDEAS

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    3. Mike Thelwall & Kevan Buckley, 2013. "Topic‐based sentiment analysis for the social web: The role of mood and issue‐related words," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(8), pages 1608-1617, August.
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    5. Prabowo, Rudy & Thelwall, Mike, 2009. "Sentiment analysis: A combined approach," Journal of Informetrics, Elsevier, vol. 3(2), pages 143-157.
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