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Effectiveness of data-driven induction of semantic spaces and traditional classifiers for sarcasm detection

Published online by Cambridge University Press:  01 April 2019

Mattia Antonino Di Gangi ,
Giosué Lo Bosco Open the ORCID record for Giosué Lo Bosco [Opens in a new window]  and
Giovanni Pilato
Mattia Antonino Di Gangi
Affiliation:
Fondazione Bruno Kessler and Università degli Studi di Trento, Trento, Italy
Giosué Lo Bosco*
Affiliation:
Dipartimento di Matematica e Informatica, Università degli Studi di Palermo, Palermo, Italy
Giovanni Pilato
Affiliation:
Italian National Research Council, ICAR-CNR, Istituto di Calcolo e Reti ad alte prestazioni, Palermo, Italy
*
*Corresponding author. Email: [email protected]
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Abstract

Irony and sarcasm are two complex linguistic phenomena that are widely used in everyday language and especially over the social media, but they represent two serious issues for automated text understanding. Many labeled corpora have been extracted from several sources to accomplish this task, and it seems that sarcasm is conveyed in different ways for different domains. Nonetheless, very little work has been done for comparing different methods among the available corpora. Furthermore, usually, each author collects and uses their own datasets to evaluate his own method. In this paper, we show that sarcasm detection can be tackled by applying classical machine-learning algorithms to input texts sub-symbolically represented in a Latent Semantic space. The main consequence is that our studies establish both reference datasets and baselines for the sarcasm detection problem that could serve the scientific community to test newly proposed methods.

Keywords

natural language processingsemantic spacesmachine learningsarcasm detectionirony detection
Type
Article
Information
Natural Language Engineering , Volume 25 , Issue 2 , March 2019 , pp. 257 - 285
Copyright
© Cambridge University Press 2019 

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