#SarcasmDetection is soooo general! Towards a Domain-Independent Approach for Detecting Sarcasm

TL;DR

This paper proposes a domain-independent sarcasm detection method using a general feature set and domain adaptation, achieving high F1 scores across different domains and outperforming prior work.

Contribution

It introduces a novel, general feature set and a domain adaptation approach for sarcasm detection, enabling effective transfer across diverse domains.

Findings

Achieved an F1 score of 0.780 with domain adaptation.

Outperformed previous state-of-the-art results on the same domain.

Demonstrated robustness across multiple domains.

Abstract

Automatic sarcasm detection methods have traditionally been designed for maximum performance on a specific domain. This poses challenges for those wishing to transfer those approaches to other existing or novel domains, which may be typified by very different language characteristics. We develop a general set of features and evaluate it under different training scenarios utilizing in-domain and/or out-of-domain training data. The best-performing scenario, training on both while employing a domain adaptation step, achieves an F1 of 0.780, which is well above baseline F1-measures of 0.515 and 0.345. We also show that the approach outperforms the best results from prior work on the same target domain.