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International Journal of Information and Communication Technology Research (IJICT، جلد ۱۶، شماره ۳، صفحات ۰-۰
عنوان فارسی
چکیده فارسی مقاله
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عنوان انگلیسی A Deep Learning Approach for Sarcasm Detection on Twitter
چکیده انگلیسی مقاله 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
کلیدواژه‌های انگلیسی مقاله sarcasm detection, deep learning, sentiment analysis
نویسندگان مقاله | Mohammad Javad Shayegan
University of Science and Culture


| Sara Kojouri
University of Science and Culture
نشانی اینترنتی http://ijict.itrc.ac.ir/browse.php?a_code=A-10-4376-1&slc_lang=en&sid=1
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