Irony detection via sentiment-based transfer learning. Issue 5 (September 2019)

• Record Type: Journal Article
• Authors: Zhang, Shiwei; Zhang, Xiuzhen; Chan, Jeffrey; Rosso, Paolo
• Is Part Of: Information processing & management. Volume 56:Issue 5(2019:Sep.)
• Page Start: 1633
• Page End: 1644
• Publication Date: 2019-09
• Subjects: Irony detection -- Transfer learning; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2019.04.006
• Languages: English
• ISSNs: 0306-4573

Abstract

Highlights: Take advantage of the readily available sentiment resources to identify implicit incongruity for irony detection. Transferring deep sentiment features to a neural attention model is an effective approach to extract patterns of implicit incongruity embedded in ironic texts. Evaluate irony detection models using human-annotated and automatic hashtag-labeled datasets separately. Abstract: Irony as a literary technique is widely used in online texts such as Twitter posts. Accurate irony detection is crucial for tasks such as effective sentiment analysis. A text's ironic intent is defined by its context incongruity. For example in the phrase "I love being ignored", the irony is defined by the incongruity between the positive word "love" and the negative context of "being ignored". Existing studies mostly formulate irony detection as a standard supervised learning text categorization task, relying on explicit expressions for detecting context incongruity. In this paper we formulate irony detection instead as a transfer learning task where supervised learning on irony labeled text is enriched with knowledge transferred from external sentiment analysis resources. Importantly, we focus on identifying the hidden, implicit incongruity without relying on explicit incongruity expressions, as in "I like to think of myself as a broken down Justin Bieber – my philosophy professor." We propose three transfer learning-based approaches to using sentiment knowledge to improve the attention mechanism of recurrent neural models for capturing hidden patterns for incongruity. Our main findings are: (1) Using sentiment knowledge from external resources is a very effective approach to improving irony detection; (2) For detecting implicit incongruity, transferring deep sentiment features seems to be the most effective way. Experiments show that our proposed models outperform state-of-the-art neural models for irony detection.