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政大機構典藏 > 資訊學院 > 資訊科學系 > 會議論文 > Item 140.119/157010

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/157010

Title:	Sarcasm Detection of Facebook’s Posts Using Machine Learning Models
Authors:	邱淑怡 Chiu, Shu-I;Jhou, Ting-Wei
Contributors:	資訊系
Keywords:	Sarcasm;machine learning;deep learning;social media
Date:	2024-12
Issue Date:	2025-05-19 11:44:25 (UTC+8)
Abstract:	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.
Relation:	International Conference on Natural Language Processing and Information Retrieval, Okayama university
Data Type:	conference
DOI 連結:	https://doi.org/10.1145/3711542.3711555
DOI:	10.1145/3711542.3711555
Appears in Collections:	[資訊科學系] 會議論文

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