  In this paper, we propose a novel mechanism for enriching the feature vector,
for the task of sarcasm detection, with cognitive features extracted from
eye-movement patterns of human readers. Sarcasm detection has been a
challenging research problem, and its importance for NLP applications such as
review summarization, dialog systems and sentiment analysis is well recognized.
Sarcasm can often be traced to incongruity that becomes apparent as the full
sentence unfolds. This presence of incongruity- implicit or explicit- affects
the way readers eyes move through the text. We observe the difference in the
behaviour of the eye, while reading sarcastic and non sarcastic sentences.
Motivated by his observation, we augment traditional linguistic and stylistic
features for sarcasm detection with the cognitive features obtained from
readers eye movement data. We perform statistical classification using the
enhanced feature set so obtained. The augmented cognitive features improve
sarcasm detection by 3.7% (in terms of F-score), over the performance of the
best reported system.
