Deep learning--based models have surpassed classical machine learning--based
approaches in various text classification tasks, including sentiment analysis, news …

Automatic sarcasm detection is the task of predicting sarcasm in text. This is a crucial step to
sentiment analysis, considering prevalence and challenges of sarcasm in sentiment-bearing …

Natural language processing (NLP) applications are now more powerful and ubiquitous
than ever before. With rapidly developing (neural) models and ever-more available data …

The freedom of expression given by social media has a dark side: the growing proliferation
of abusive contents on these platforms. Misogynistic speech is a kind of abusive language …

Convolutional Neural Networks (CNNs) have recently achieved remarkably strong
performance on the practically important task of sentence classification (kim 2014 …

Sarcasm is often expressed through several verbal and non-verbal cues, eg, a change of
tone, overemphasis in a word, a drawn-out syllable, or a straight looking face. Most of the …

Sarcasm requires some shared knowledge between speaker and audience; it is a
profoundly contextual phenomenon. Most computational approaches to sarcasm detection …

We introduce a deep neural network for automated sarcasm detection. Recent work has
emphasized the need for models to capitalize on contextual features, beyond lexical and …

We introduce the Self-Annotated Reddit Corpus (SARC), a large corpus for sarcasm
research and for training and evaluating systems for sarcasm detection. The corpus has 1.3 …

The literature in automated sarcasm detection has mainly focused on lexical, syntactic and
semantic-level analysis of text. However, a sarcastic sentence can be expressed with …