Harnessing Cognitive Features for Sarcasm Detection

TL;DR

This paper introduces a novel sarcasm detection method that incorporates cognitive features from eye-movement data, significantly improving detection accuracy by capturing reader-inferred incongruity.

Contribution

It is the first to integrate eye-movement cognitive features with linguistic features for sarcasm detection, enhancing model performance.

Findings

Cognitive features improve sarcasm detection F-score by 3.7%.

Eye movement patterns differ between sarcastic and non-sarcastic sentences.

Enhanced feature set outperforms previous systems.

Abstract

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.