Multi-label Text Classification Approach for Sentence Level News Emotion Analysis

  • Plaban Kr. Bhowmick
  • Anupam Basu
  • Pabitra Mitra
  • Abhishek Prasad
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5909)

Abstract

Multiple emotions are often evoked in readers in response to text stimuli like news article. In this paper, we present a novel method for classifying news sentences into multiple emotion categories using Multi-Label K Nearest Neighbor classification technique. The emotion data consists of 1305 news sentences and the emotion classes considered are disgust, fear, happiness and sadness. Words and polarity of subject, verb and object of the sentences and semantic frames have been used as features. Experiments have been performed on feature comparison and feature selection.

Keywords

Feature Selection News Article Text Segment Word Feature Emotion Class 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Plaban Kr. Bhowmick
    • 1
  • Anupam Basu
    • 1
  • Pabitra Mitra
    • 1
  • Abhishek Prasad
    • 1
  1. 1.Indian Institute of TechnologyKharagpurIndia

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