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題名 Sarcasm Detection of Facebook’s Posts Using Machine Learning Models
作者 邱淑怡
Chiu, Shu-I;Jhou, Ting-Wei
貢獻者 資訊系
關鍵詞 Sarcasm; machine learning; deep learning; social media
日期 2024-12
上傳時間 19-五月-2025 11:44:25 (UTC+8)
摘要 The coronavirus disease 2019 (COVID-19) has brought massive challenges to the world, altering people's lives. We conducted a study on people's mental states during Taiwan's National Epidemic Level 3 Alert in 2021. On May 22, 2021, during a regular press conference held by the Taiwan Centers for Disease Control (CDC), the Minister of Health and Welfare introduced the term 'Retrospective Adjustment', which left the entire population in shock. Our approach consists of a two-stage task. Constructing the model is the first stage and detecting sarcasm is the second. First, we integrated TCNN and BiLSTM models. Second, we focused on misclassified by the model and performed feature engineering based on these misclassified data. After constructing features, we performed well using machine learning models. Finally, the experimental results show that our approach performs well in detecting sarcasm using linguistic and lexical-based features. In the second stage, the LSTM model detects sarcasm, achieving a performance of 0.73. We integrate the results of two stages to adjust accuracy. By improving the accuracy of the misclassified data to 0.6, the overall accuracy for negative posts has increased to 0.76.
關聯 International Conference on Natural Language Processing and Information Retrieval, Okayama university
資料類型 conference
DOI https://doi.org/10.1145/3711542.3711555
dc.contributor 資訊系
dc.creator (作者) 邱淑怡
dc.creator (作者) Chiu, Shu-I;Jhou, Ting-Wei
dc.date (日期) 2024-12
dc.date.accessioned 19-五月-2025 11:44:25 (UTC+8)-
dc.date.available 19-五月-2025 11:44:25 (UTC+8)-
dc.date.issued (上傳時間) 19-五月-2025 11:44:25 (UTC+8)-
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/157010-
dc.description.abstract (摘要) The coronavirus disease 2019 (COVID-19) has brought massive challenges to the world, altering people's lives. We conducted a study on people's mental states during Taiwan's National Epidemic Level 3 Alert in 2021. On May 22, 2021, during a regular press conference held by the Taiwan Centers for Disease Control (CDC), the Minister of Health and Welfare introduced the term 'Retrospective Adjustment', which left the entire population in shock. Our approach consists of a two-stage task. Constructing the model is the first stage and detecting sarcasm is the second. First, we integrated TCNN and BiLSTM models. Second, we focused on misclassified by the model and performed feature engineering based on these misclassified data. After constructing features, we performed well using machine learning models. Finally, the experimental results show that our approach performs well in detecting sarcasm using linguistic and lexical-based features. In the second stage, the LSTM model detects sarcasm, achieving a performance of 0.73. We integrate the results of two stages to adjust accuracy. By improving the accuracy of the misclassified data to 0.6, the overall accuracy for negative posts has increased to 0.76.
dc.format.extent 103 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) International Conference on Natural Language Processing and Information Retrieval, Okayama university
dc.subject (關鍵詞) Sarcasm; machine learning; deep learning; social media
dc.title (題名) Sarcasm Detection of Facebook’s Posts Using Machine Learning Models
dc.type (資料類型) conference
dc.identifier.doi (DOI) 10.1145/3711542.3711555
dc.doi.uri (DOI) https://doi.org/10.1145/3711542.3711555