Results 1 to 10 of about 1,585 (156)

Intermediate-Task Transfer Learning with BERT for Sarcasm Detection

Mathematics, 2022
Sarcasm detection plays an important role in natural language processing as it can impact the performance of many applications, including sentiment analysis, opinion mining, and stance detection.
Edoardo Savini, Cornelia Caragea
doaj   +3 more sources

An emoji centric approach to sarcasm detection in online discourse [PDF]

Scientific Reports
Sarcasm detection has gained significance in sentiment analysis, especially when social media is rife with cyberbullying and trolling. Emojis have garnered researchers’ interest as they are polysemic.
V. Grover, H. Banati
doaj   +2 more sources

Self-attention bidirectional long Short-Term memory assisted natural language processing on sarcasm detection and classification in social media platforms [PDF]

Scientific Reports
Sarcasm is a form of irony that expresses negative opinions. Sarcasm poses a linguistic problem owing to its symbolic nature, where deliberate meaning challenges correct understanding.
Jihen Majdoubi, Taghreed Ali Alsudais, Abeer S. Almogren, Wafi Bedewi, Majdy M. Eltahir, Mohammed Alshahrani, Shouki A. Ebad, Mukhtar Ghaleb   +7 more
doaj   +2 more sources

Sarcasm detection using news headlines dataset

AI Open, 2023
Sarcasm has been an elusive concept for humans. Due to interesting linguistic properties, sarcasm detection has gained traction of the Natural Language Processing (NLP) research community in the past few years.
Rishabh Misra, Prahal Arora
doaj   +3 more sources

A multi-modal sarcasm detection model based on cue learning [PDF]

Scientific Reports
The rapid proliferation of internet data, particularly through social media, has amplified the need for effective sentiment analysis, including the complex task of sarcasm detection.
Ming Lu, Zhiqiang Dong, Ziming Guo, Xiaoming Zhang, Xinxi Lu, Tianbo Wang, Litian Zhang   +6 more
doaj   +2 more sources

Detecting Sarcasm in Multiple Modalities by Leveraging Sarcasm Type Correlations

IEEE Access
Sarcasm detection is essential for accurately interpreting communication in applications such as dialogue systems. However, most existing approaches treat sarcasm as a single phenomenon and ignore the linguistic distinction between illocutionary ...
Jeremy Chang, Chia-Yun Hsu, Chung-Hsien Wu   +2 more
doaj   +2 more sources

Graph convolutional network with reinforced dependency graph and denoising mechanism for sarcasm detection [PDF]

Scientific Reports
The widespread presence of sarcasm in social media presents significant challenges to sentiment analysis and public opinion monitoring, making accurate sarcasm detection particularly important.
Pingping Yan, Tianbo An, Jiaxuan Yu, Jinjin An, Daqun Tong, Jingrui Wang   +5 more
doaj   +2 more sources

Enhancing sarcasm detection on social media: A comprehensive study using LLMs and BERT with multi-headed attention on SARC. [PDF]

PLoS ONE
Sarcasm detection in natural language processing (NLP) remains a complex challenge, especially in social media, where contextual clues are often subtle.
Lihong Zhang, Muhammad Faseeh, Syed Shehryar Ali Naqvi, Liang Hu, Anwar Ghani   +4 more
doaj   +2 more sources

A contextual-based approach for sarcasm detection. [PDF]

Sci Rep
AbstractSarcasm is a perplexing form of human expression that presents distinct challenges in understanding. The problem of sarcasm detection has centered around analyzing individual utterances in isolation which may not provide a comprehensive understanding of the speaker’s sarcastic intent.
Helal NA, Hassan A, Badr NL, Afify YM.
europepmc   +4 more sources

Sarcasm Detection with and without #Sarcasm: Data Science Approach

International Journal of Information Science and Management, 2022
Natural languages usually contain context, which is difficult for a machine to understand. Sentiment analysis is a contextual mining technique often used in NLP to identify, understand and extract subjective information in texts, such as people’s ...
Rupali Amit Bagate, R Suguna
doaj   +1 more source