Volume 16, Issue 3 (7-2024)                   itrc 2024, 16(3): 45-53 | Back to browse issues page

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Shayegan M J, Kojouri S. A Deep Learning Approach for Sarcasm Detection on Twitter. itrc 2024; 16 (3) :45-53
URL: http://journal.itrc.ac.ir/article-1-613-en.html
A Deep Learning Approach for Sarcasm Detection on Twitter
Mohammad Javad Shayegan 1, Sara Kojouri2
1- Department of Computer Engineering University of Science and Culture Tehran, Iran , showcaran@gmail.com
2- Department of Computer Engineering University of Science and Culture Tehran, Iran
Abstract:   (1565 Views)
Sarcasm is a form of speech in which a person expresses his opinion implicitly. We may encounter a seemingly positive sentence in sarcasm, but the speaker has a contrary opinion. Sarcasm can be recognized in spoken language based on body language and the tone of voice. However, the lack of these features makes it difficult to recognize sarcasm in text. In recent years, Twitter has attracted much attention and has become a popular platform for sharing opinions and viewpoints. It is also common for people to use sarcasm on Twitter as an indirect means of expressing their opinions. The presence of sarcasm in the text makes it difficult to recognize the sentiment. Thus, it is necessary and inevitable to have solutions that can detect sarcasm. This study aims to provide a solution for detecting sarcasm on Twitter using deep learning approaches. This study used two Twitter datasets containing balance and imbalance data for modeling. The main idea of this research is to use additional features such as sentimental features, subjectivity, number of hashtags, and punctuation along with features that deep learning algorithms automatically extract. The impact of each feature is reported in the paper. In this research, GRU-Capsule based neural network has been used. According to the results, the proposed model has improved accuracy by 5% for balanced data and by 2% for imbalanced data.
Keywords: sarcasm detection, deep learning, sentiment analysis
Full-Text [PDF 899 kb]   (206 Downloads)    
Type of Study: Research | Subject: Information Technology
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