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題名 Deep Learning Models for Predicting Political Tendency in Facebook Posts Incorporating Texts and Emojis
作者 邱淑怡
Chiu, Shu-I
貢獻者 資訊系
關鍵詞 Natural language processing; deep learning; sentiment analysis; emoji; Facebook; social networks
日期 2024-08
上傳時間 7-Jan-2025 09:35:43 (UTC+8)
摘要 COVID-19 has brought massive challenges to the world, altering people's lives. We conducted a study on people's minds during Taiwan's National Epidemic 3-Level 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. This study focuses on analyzing social media posts during the outbreak, specifically 6,022 selected Facebook posts that mention ‘Retrospective Adjustment’ between May 22, 2021, and May 25, 2021. Various models are utilized to classify the sentiment categories of these posts, considering both texts and emojis. We compare the performance of the classification models using posts with only texts and both texts and emojis. The LSTM and BiLSTM are suitable for processing posts containing texts and emojis. Conversely, the BERT model performs better when it includes only text. In the case of the BERT model with only text, the F1-score reaches 0.8 for the positive and objective posts. However, the BERT model does not perform well for negative posts. Our results indicate a lack of sensitivity towards the contextual effects of negation in the BERT model.
關聯 6th International Conference on Data-driven Optimization of Complex Systems, 西湖大學
資料類型 conference
DOI https://doi.org/10.1109/DOCS63458.2024.10704299
dc.contributor 資訊系
dc.creator (作者) 邱淑怡
dc.creator (作者) Chiu, Shu-I
dc.date (日期) 2024-08
dc.date.accessioned 7-Jan-2025 09:35:43 (UTC+8)-
dc.date.available 7-Jan-2025 09:35:43 (UTC+8)-
dc.date.issued (上傳時間) 7-Jan-2025 09:35:43 (UTC+8)-
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/155062-
dc.description.abstract (摘要) COVID-19 has brought massive challenges to the world, altering people's lives. We conducted a study on people's minds during Taiwan's National Epidemic 3-Level 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. This study focuses on analyzing social media posts during the outbreak, specifically 6,022 selected Facebook posts that mention ‘Retrospective Adjustment’ between May 22, 2021, and May 25, 2021. Various models are utilized to classify the sentiment categories of these posts, considering both texts and emojis. We compare the performance of the classification models using posts with only texts and both texts and emojis. The LSTM and BiLSTM are suitable for processing posts containing texts and emojis. Conversely, the BERT model performs better when it includes only text. In the case of the BERT model with only text, the F1-score reaches 0.8 for the positive and objective posts. However, the BERT model does not perform well for negative posts. Our results indicate a lack of sensitivity towards the contextual effects of negation in the BERT model.
dc.format.extent 111 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) 6th International Conference on Data-driven Optimization of Complex Systems, 西湖大學
dc.subject (關鍵詞) Natural language processing; deep learning; sentiment analysis; emoji; Facebook; social networks
dc.title (題名) Deep Learning Models for Predicting Political Tendency in Facebook Posts Incorporating Texts and Emojis
dc.type (資料類型) conference
dc.identifier.doi (DOI) 10.1109/DOCS63458.2024.10704299
dc.doi.uri (DOI) https://doi.org/10.1109/DOCS63458.2024.10704299