Sarcasm Detection Using Soft Attention-Based Bidirectional Long Short-Term Memory Model With Convolution Network
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DOI: 10.1109/access.2019.2899260
Abstract: A large community of research has been developed in recent years to analyze social media and social networks, with the aim of understanding, discovering insights, and exploiting the available information. The focus has shifted from… read more here.
Keywords: satt blstm; convnet; attention; sarcasm detection ... See more keywords
Sarcasm Detection Using Deep Learning With Contextual Features
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DOI: 10.1109/access.2021.3076789
Abstract: Our work focuses on detecting sarcasm in tweets using deep learning extracted features combined with contextual handcrafted features. A feature set is extracted from a Convolutional Neural Network (CNN) architecture before it is combined with… read more here.
Keywords: sarcasm detection; detection using; feature sets; using deep ... See more keywords
A Comprehensive Survey on Arabic Sarcasm Detection: Approaches, Challenges and Future Trends
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DOI: 10.1109/access.2023.3247427
Abstract: On social media platforms, it is essential to express one’s thoughts, opinions, and reviews. One of the most widely used linguistic forms to criticize or express a person’s ideas with ridicule is sarcasm, where the… read more here.
Keywords: survey arabic; arabic sarcasm; detection; comprehensive survey ... See more keywords
CFN: A Complex-Valued Fuzzy Network for Sarcasm Detection in Conversations
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Fuzzy Systems"
DOI: 10.1109/tfuzz.2021.3072492
Abstract: Sarcasm detection in conversation, a theoretically and practically challenging artificial intelligence task, aims to discover elusively ironic, contemptuous, and metaphoric information implied in daily conversations. Most of the recent approaches in sarcasm detection have neglected… read more here.
Keywords: valued fuzzy; sarcasm detection; complex valued; fuzzy network ... See more keywords
Sarcasm detection using machine learning algorithms in Twitter: A systematic review
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DOI: 10.1177/1470785320921779
Abstract: Recognizing both literal and figurative meanings is crucial to understanding users’ opinions on various topics or events in social media. Detecting the sarcastic posts on social media has received much attention recently, particularly because sarcastic… read more here.
Keywords: machine; machine learning; learning algorithms; twitter ... See more keywords
Enhanced Semantic Representation Learning for Sarcasm Detection by Integrating Context-Aware Attention and Fusion Network
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DOI: 10.3390/e25060878
Abstract: Sarcasm is a sophisticated figurative language that is prevalent on social media platforms. Automatic sarcasm detection is significant for understanding the real sentiment tendencies of users. Traditional approaches mostly focus on content features by using… read more here.
Keywords: fusion network; detection; context aware; aware attention ... See more keywords