Jointly Learning Sentimental Clues and Context Incongruity for Sarcasm Detection

Chen, Wangqun; Lin, Fuqiang; Zhang, Xuan; Li, Guowei; Liu, Bo

doi:10.1109/access.2022.3169864

Cited by 5 publications

(2 citation statements)

References 37 publications

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“…According to recent studies, the two language traits of sentiment and incongruity information are helpful for sarcasm identification. Chen et al [7] suggested a multi-task learning approach that models context incongruity while integrating semantic data and soft sentiment labels to incorporate sentiment cues. The suggested model performs better for the sarcasm detection job with the use of sentiment hints and incongruity information, according to experimental results on datasets.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhanced Deep Learning-Based Model for Sentiment Analysis to Identify Sarcasm Appeared in the News

Gupta,

Chatterjee,

Gupta

2023

cai

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recognizing it, leading to a misinterpretation of the news and spreading misinformation. As a result, there is an urgent need for technology that can automatically identify sarcasm with high accuracy. Recent studies in this domain have revealed a need for a robust and efficient model. Deep learning approaches have proven to be effective in sarcasm detection. In this work, we propose a novel two-stage model that uses a word-embedding technique to select relevant features followed by an advanced deep-learning architecture to classify sarcasm in news headlines. Our proposed method demonstrates promising results in identifying sarcasm in text with an accuracy rate of approximately 97 %. We have fine-tuned the hyper-parameters to increase the precision level, which enhances the efficacy of our model. Our work provides a significant contribution to the field of NLP by presenting a reliable and effective model for sarcasm detection. The comparison of our model with recent advancements indicates that our approach outperforms them. By using our model, readers can avoid misinterpretations and the spreading of misinformation. Therefore, our work can have a positive impact on society, and we believe that it can inspire future research in the field of sarcasm detection.

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Section: Literature Reviewmentioning

confidence: 99%

Enhanced Deep Learning-Based Model for Sentiment Analysis to Identify Sarcasm Appeared in the News

Gupta,

Chatterjee,

Gupta

2023

cai

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“…Across the social networking sites, the surge for user generated content as drastically increased. And among all, twitter is the social network platform that records huge volume of opinionated information [7,8].…”

Section: Introductionmentioning

confidence: 99%

Neural Network-Based Cyber-Bullying and Cyber-Aggression Detection Using Twitter(X) Text

Agbaje,

Afolabi

2024

RIA

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A survey of automatic sarcasm detection: Fundamental theories, formulation, datasets, detection methods, and opportunities

Chen,

Lin,

et al. 2024

Neurocomputing

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Jointly Learning Sentimental Clues and Context Incongruity for Sarcasm Detection

Cited by 5 publications

References 37 publications

Enhanced Deep Learning-Based Model for Sentiment Analysis to Identify Sarcasm Appeared in the News

Enhanced Deep Learning-Based Model for Sentiment Analysis to Identify Sarcasm Appeared in the News

Neural Network-Based Cyber-Bullying and Cyber-Aggression Detection Using Twitter(X) Text

A survey of automatic sarcasm detection: Fundamental theories, formulation, datasets, detection methods, and opportunities

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