A Hybrid Mood Classification Approach for Blog Text

  • Yuchul Jung
  • Hogun Park
  • Sung Hyon Myaeng
Conference paper

DOI: 10.1007/978-3-540-36668-3_141

Volume 4099 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Jung Y., Park H., Myaeng S.H. (2006) A Hybrid Mood Classification Approach for Blog Text. In: Yang Q., Webb G. (eds) PRICAI 2006: Trends in Artificial Intelligence. PRICAI 2006. Lecture Notes in Computer Science, vol 4099. Springer, Berlin, Heidelberg

Abstract

As an effort to detect the mood of a blog, regardless of the length and writing style, we propose a hybrid approach to detecting blog text’s mood, which incorporates commonsense knowledge obtained from the general public (ConceptNet) and the Affective Norms English Words (ANEW) list. Our approach picks up blog text’s unique features and compute simple statistics such as term frequency, n-gram, and point-wise mutual information (PMI) for the SVM classification method. In addition, to catch mood transitions in a given blog text, we developed a paragraph-level segmentation based on a mood flow analysis using a revised version of the GuessMood operation of ConceptNet and an ANEW-based affective sensing module. For evaluation, a mood corpus comprised of real blog texts has been built semi-automatically. Our experiments using the corpus show meaningful results for 4 mood types: happy, sad, angry, and fear.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yuchul Jung
    • 1
  • Hogun Park
    • 1
  • Sung Hyon Myaeng
    • 1
  1. 1.School of EngineeringInformation and Communications University, South KoreaDaejeonKorea