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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References
Mishne, G.: Experiments with Mood Classification in Blog Posts. In: Style 2005 – the 1st Workhops on Stylistic Analysis of Text for Information Access, at ACM SIGIR 2005 (2005)
Liu, H., Singh, P.: ConceptNet – A practical commonsense reasoning tool-kit. BT Technology Journal, 211–226 (2004)
Bradley, M.M., Lang, P.J.: Affective norms for English words (ANEW). Gainesville, FL. The NIMH Center for the Study of Emotion and Attention, University of Florida (1999)
Joachims, T.: Text Categorization with Support Vector Machines: Learning with Many Relevant Features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998)
Singh, P., Lin, T., Mueller, E.T., Lim, G., Perkins, T., Li Zhu, W.: Open Mind Common Sense: Knowledge acquisition from the general public. In: Proc. of the First Int. Conf. on Ontologies, Databases, and Applications of Semantics for Large Scale Information Systems (2002)
Liu, H., Lieberman, H., Selker: A Model of Textual Affect Sensing using Real-World Knowledge. In: Proc. of the 2003 Int. Conf. on Intelligent User Interfaces, IUI 2003 (2003)
Read, J.: Recognising affect in text using pointwise-mutual information. Master’s thesis, University of Sussex (2004)
Mehrabian, A.: Framework for a comprehensive description and measurement of emotional states. Genetic, Soccial, and General Psychology Monographs 121(3), 339–361 (1995)
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© 2006 Springer-Verlag Berlin Heidelberg
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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. https://doi.org/10.1007/978-3-540-36668-3_141
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DOI: https://doi.org/10.1007/978-3-540-36668-3_141
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36667-6
Online ISBN: 978-3-540-36668-3
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