IEEE Computational Intelligence Magazine - November 2019 - 77

Application
Notes

Diego Reforgiato Recupero
Department of Computer Science, University of Cagliari, Cagliari, ITALY
Mehwish Alam
FIZ Karlsruhe-Leibniz Institute for Information Infrastructure, Karlsruhe, GERMANY
Davide Buscaldi
Laboratoire d'Informatique de Paris-Nord, Université Paris 13, Paris, FRANCE
Aude Grezka
Laboratoire d'Informatique de Paris-Nord, Université Paris 13, Paris, FRANCE
Farideh Tavazoee
Department of Economic and Business Science, University of Cagliari,
Cagliari, ITALY

Frame-Based Detection of Figurative Language in Tweets

Abstract

Digital Object Identifier 10.1109/MCI.2019.2937614
Date of current version: 14 October 2019

1556-603X/19©2019IEEE

IMAGE LICENSED BY INGRAM PUBLISHING

T

his paper analyzes the problem of
figurative language detection on
social media, with a focus on the
use of semantic features for identifying
irony and sarcasm. Framester, a novel
resource that acts as a hub between
FrameNet, WordNet, VerbNet, BabelNet,
DBpedia, Yago, DOLCE-Zero and others, has been used to extract semantic features from text. These semantic features
are used to enrich the representations of
tweets with event information using
frames and word senses in addition to
lexical units. The data set used for experimentation purposes contains tweets taken
from different corpora including both
figurative (containing irony and sarcasm)
and non-figurative language. Two major
tasks were performed: (i) detecting figurative language in tweets in a dataset containing both figurative and non-figurative
tweets, (ii) classifying tweets containing
irony and sarcasm. A 10-fold cross-validation experiment shows that the obtained
accuracy for both tasks increases significantly when the semantic features such as
linguistic frames and word senses are used
in addition to lexical units, indicating that
they may be important clues for figurative language. The approach was devel-

oped on top of Apache Spark so that it is
easily scalable to much higher volumes of
data, allowing for real-time analysis.
1. Introduction

The use of figurative language is rapidly growing in online textual resources
including product reviews and social
media, making Sentiment Analysis (SA)
[1]-[4] one of the most important and
trending research problems. SA often
involves assessing the polarity (positive
Corresponding Author: Diego Reforgiato Recupero
(diego.reforgiato@unica.it)

or negative) of an opinion holder on a
particular topic within some text. Furthermore, sentiment polarity may not be
able to fully express the affective meaning (e.g., emotion) that writers want to
give to an object or to any of its related
features: therefore, there is a need for a
stronger and more effective evaluation
that is able to show writers' emotions in
text. The task of emotion detection is to
identify the emotion conveyed within a
text (e.g., happiness, sadness, anger, etc.).
Opinion mining and emotion detection
are important for a variety of applications and industr ies, from helping

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IEEE Computational Intelligence Magazine - November 2019

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