3 research outputs found
"Having 2 hours to write a paper is fun!": Detecting Sarcasm in Numerical Portions of Text
Sarcasm occurring due to the presence of numerical portions in text has been
quoted as an error made by automatic sarcasm detection approaches in the past.
We present a first study in detecting sarcasm in numbers, as in the case of the
sentence 'Love waking up at 4 am'. We analyze the challenges of the problem,
and present Rule-based, Machine Learning and Deep Learning approaches to detect
sarcasm in numerical portions of text. Our Deep Learning approach outperforms
four past works for sarcasm detection and Rule-based and Machine learning
approaches on a dataset of tweets, obtaining an F1-score of 0.93. This shows
that special attention to text containing numbers may be useful to improve
state-of-the-art in sarcasm detection
A Robust Deep Ensemble Classifier for Figurative Language Detection
Recognition and classification of Figurative Language (FL) is an open problem
of Sentiment Analysis in the broader field of Natural Language Processing (NLP)
due to the contradictory meaning contained in phrases with metaphorical
content. The problem itself contains three interrelated FL recognition tasks:
sarcasm, irony and metaphor which, in the present paper, are dealt with
advanced Deep Learning (DL) techniques. First, we introduce a data
prepossessing framework towards efficient data representation formats so that
to optimize the respective inputs to the DL models. In addition, special
features are extracted in order to characterize the syntactic, expressive,
emotional and temper content reflected in the respective social media text
references. These features aim to capture aspects of the social network user's
writing method. Finally, features are fed to a robust, Deep Ensemble Soft
Classifier (DESC) which is based on the combination of different DL techniques.
Using three different benchmark datasets (one of them containing various FL
forms) we conclude that the DESC model achieves a very good performance, worthy
of comparison with relevant methodologies and state-of-the-art technologies in
the challenging field of FL recognition.Comment: Published in Engineering Applications of Neural Networks (EANN)-201
A Transformer-based approach to Irony and Sarcasm detection
Figurative Language (FL) seems ubiquitous in all social-media discussion
forums and chats, posing extra challenges to sentiment analysis endeavors.
Identification of FL schemas in short texts remains largely an unresolved issue
in the broader field of Natural Language Processing (NLP), mainly due to their
contradictory and metaphorical meaning content. The main FL expression forms
are sarcasm, irony and metaphor. In the present paper we employ advanced Deep
Learning (DL) methodologies to tackle the problem of identifying the
aforementioned FL forms. Significantly extending our previous work [71], we
propose a neural network methodology that builds on a recently proposed
pre-trained transformer-based network architecture which, is further enhanced
with the employment and devise of a recurrent convolutional neural network
(RCNN). With this set-up, data preprocessing is kept in minimum. The
performance of the devised hybrid neural architecture is tested on four
benchmark datasets, and contrasted with other relevant state of the art
methodologies and systems. Results demonstrate that the proposed methodology
achieves state of the art performance under all benchmark datasets,
outperforming, even by a large margin, all other methodologies and published
studies.Comment: Neural Comput & Applic (2020