Predicting Readers’ Sarcasm Understandability by Modeling Gaze Behavior

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


In the previous two chapters, we demonstrated how cognitive effort in text annotation can be assessed by utilizing cognitive information obtained from readers’/annotators’ eye-gaze patterns. While our models are, to some extent, effective in modeling various forms of complexities at the textual side, we observed that cognitive information can also be useful to model the ability of a reader to understand/comprehend the given reading material. This observation was quite clear in our sentiment annotation experiment (discussed in Chap. 3), where the eye-movement patterns of some of our annotators appeared to be subtle when the text had linguistic nuances like sarcasm, which the annotators failed to recognize. This motivated us to work on a highly specific yet important problem of sarcasm understandability prediction—a starting step toward an even more important problem of modeling text comprehensibility.


Modeling Gaze Behavior Text Side Linguistic Nuances Scanpath Understanding Irony 
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 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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