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Harnessing Cognitive Features for Sentiment Analysis and Sarcasm Detection

  • Abhijit MishraEmail author
  • Pushpak Bhattacharyya
Chapter
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Part of the Cognitive Intelligence and Robotics book series (CIR)

Abstract

This chapter begins the second part of the thesis in which we demonstrate how cognitive information can be harnessed for NLP, specifically for text classification. We discuss several possibilities of extracting features from the eye-movement patterns of annotators and injecting them in popular NLP frameworks. We show that features derived from such forms of cognitive data (referred to as cognition-driven features or cognitive features), when augmented with traditional linguistic features used for well-known supervised machine learning-based NLP systems, improve the performance of such systems. We stick to the tasks of sentiment analysis and sarcasm detection—two well-known problems in text classification.

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© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.India Research LabIBM ResearchBangaloreIndia
  2. 2.Indian Institute of Technology PatnaPatnaIndia

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