A2Text-Net: A Novel Deep Neural Network for Sarcasm Detection

Liu, Liyuan; Priestley, Jennifer L.; Zhou, Yue; Ray, Herman E.; Han, Meng

doi:10.1109/cogmi48466.2019.00025

Cited by 37 publications

(19 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Chatterjee et al [50] took the context of the utterance into consideration, and proposed a deep learning based approach. Liu et al [51] proposed a deep neural network, called A2Text-Net, to mimic the face-to-face speech, which integrated auxiliary clues such as punctuations, part of speech (POS), emoji, etc., to improve the performance of sarcasm detection.…”

Section: A Sarcasm Analysismentioning

confidence: 99%

CFN: A Complex-Valued Fuzzy Network for Sarcasm Detection in Conversations

Zhang

Liu

et al. 2021

IEEE Trans. Fuzzy Syst.

View full text Add to dashboard Cite

Sarcasm detection in conversation (SDC), a theoretically and practically challenging artificial intelligence (AI) task, aims to discover elusively ironic, contemptuous and metaphoric information implied in daily conversations. Most of the recent approaches in sarcasm detection have neglected the intrinsic vagueness and uncertainty of human language in emotional expression and understanding. To address this gap, we propose a complex-valued fuzzy network (CFN) by leveraging the mathematical formalisms of quantum theory (QT) and fuzzy logic. In particular, the target utterance to be recognized is considered as a quantum superposition of a set of separate words. The contextual interaction between adjacent utterances is described as the interaction between a quantum system and its surrounding environment, constructing the quantum composite system, where the weight of interaction is determined by a fuzzy membership function. In order to model both the vagueness and uncertainty, the aforementioned superposition and composite systems are mathematically encapsulated in a density matrix. Finally, a quantum fuzzy measurement is performed on the density matrix of each utterance to yield the probabilistic outcomes of sarcasm recognition. Extensive experiments are conducted on the MUStARD and the 2020 sarcasm detection Reddit track datasets, and the results show that our model outperforms a wide range of strong baselines.

show abstract

Section: A Sarcasm Analysismentioning

confidence: 99%

CFN: A Complex-Valued Fuzzy Network for Sarcasm Detection in Conversations

Zhang

Liu

et al. 2021

IEEE Trans. Fuzzy Syst.

View full text Add to dashboard Cite

show abstract

“…To create a dataset, they selected sarcastic and non-sarcastic videos. [33] proposed a new deep neural network method for sarcasm detection known as A2Text-Net. They integrated the auxiliary variables like POS, emoji, punctuations and numerals etc., to enhance the performance of sarcasm detection classifier.…”

Section: Literature Reviewmentioning

confidence: 99%

A Survey on Sarcasm Detection Approaches

Aboobaker¹,

Ilavarasan²

2020

INDJCSE

View full text Add to dashboard Cite

“…With the advent of deep-learning, recent works [ 5 , 6 , 7 , 8 , 9 ], leverage neural networks to learn both lexical and contextual features, eliminating the need for hand-crafted features. In these works, word embeddings are incorporated to train deep convolutional, recurrent, or attention-based neural networks to achieve state-of-the-art results on multiple large-scale datasets.…”

Section: Introductionmentioning

confidence: 99%

Interpretable Multi-Head Self-Attention Architecture for Sarcasm Detection in Social Media

Akula

Garibay

2021

Entropy

View full text Add to dashboard Cite

With the online presence of more than half the world population, social media plays a very important role in the lives of individuals as well as businesses alike. Social media enables businesses to advertise their products, build brand value, and reach out to their customers. To leverage these social media platforms, it is important for businesses to process customer feedback in the form of posts and tweets. Sentiment analysis is the process of identifying the emotion, either positive, negative or neutral, associated with these social media texts. The presence of sarcasm in texts is the main hindrance in the performance of sentiment analysis. Sarcasm is a linguistic expression often used to communicate the opposite of what is said, usually something that is very unpleasant, with an intention to insult or ridicule. Inherent ambiguity in sarcastic expressions make sarcasm detection very difficult. In this work, we focus on detecting sarcasm in textual conversations from various social networking platforms and online media. To this end, we develop an interpretable deep learning model using multi-head self-attention and gated recurrent units. The multi-head self-attention module aids in identifying crucial sarcastic cue-words from the input, and the recurrent units learn long-range dependencies between these cue-words to better classify the input text. We show the effectiveness of our approach by achieving state-of-the-art results on multiple datasets from social networking platforms and online media. Models trained using our proposed approach are easily interpretable and enable identifying sarcastic cues in the input text which contribute to the final classification score. We visualize the learned attention weights on a few sample input texts to showcase the effectiveness and interpretability of our model.

show abstract

A2Text-Net: A Novel Deep Neural Network for Sarcasm Detection

Cited by 37 publications

References 31 publications

CFN: A Complex-Valued Fuzzy Network for Sarcasm Detection in Conversations

CFN: A Complex-Valued Fuzzy Network for Sarcasm Detection in Conversations

A Survey on Sarcasm Detection Approaches

Interpretable Multi-Head Self-Attention Architecture for Sarcasm Detection in Social Media

Contact Info

Product

Resources

About