A Classifier Based Approach to Emotion Lexicon Construction

  • Dipankar Das
  • Soujanya Poria
  • Sivaji Bandyopadhyay
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7337)


The present task of developing an emotion lexicon shows the differences from the existing solutions by considering the definite as well as fuzzy connotation of the emotional words into account. A weighted lexical network has been developed on the freely available ISEAR dataset using the co-occurrence threshold. Two methods were applied on the network, a supervised method that predicts the definite emotion orientations of the words which received close or equal membership values from the first method, Fuzzy c-means clustering. The kernel functions of the two methods were modified based on the similarity based edge weights, Point wise Mutual Information (PMI) and universal Law of Gravitation (LGr) between the word pairs. The system achieves the accuracy of 85.92% in identifying emotion orientations of the words from the WordNet Affect based lexical network.


Emotion orientations ISEAR Fuzzy Clustering SVM PMI Law of Gravitation WordNet Affect 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Strapparava, C., Valitutti, A.: Wordnet affect: an affective extension of wordnet. Language Resource and Evaluation (2004)Google Scholar
  2. 2.
    Turney, P.D., Littman, M.L.: Measuring praise and criticism: Inference of semantic orientation from association. ACM TIS 21(4), 315–346 (2003)CrossRefGoogle Scholar
  3. 3.
    Das, D., Bandyopadhyay, S.: Analyzing Emotional Statements – Roles of General and Physiological Variables. In: The SAAIP Workshop, 5th IJCNLP, pp. 59–67 (2011)Google Scholar
  4. 4.
    Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algoritms. Plenum Press, New York (1981)CrossRefGoogle Scholar
  5. 5.
    Joachims, T.: Text Categorization with Support Machines: Learning with Many Relevant Features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  6. 6.
    Scherer, K.R.: What are emotions? And how can they be measured? Social Science Information 44(4), 693–727 (2005)CrossRefGoogle Scholar
  7. 7.
    Miller, A.G.: WordNet: a lexical database for English. Communications of the ACM 38(11), 39–41 (1995)CrossRefGoogle Scholar
  8. 8.
    Cervantes, J., Li, X., Yu, W.: Support Vector Machine Classification Based on Fuzzy Clustering for Large Data Sets. In: Gelbukh, A., Reyes-Garcia, C.A. (eds.) MICAI 2006. LNCS (LNAI), vol. 4293, pp. 572–582. Springer, Heidelberg (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Dipankar Das
    • 1
  • Soujanya Poria
    • 1
  • Sivaji Bandyopadhyay
    • 1
  1. 1.Department of Computer Science and EngineeringJadavpur UniversityKolkataIndia

Personalised recommendations