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研究生(外文):Tsai, Yun-Sheng
論文名稱(外文):Suicide Risk Assessment using Word-Level Model with Dictionary-Based Risky Posts Selection
指導教授(外文):Chen, Arbee L.P.
口試委員(外文):Fan, Yao-ChungHsu, Jia-Lien
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在本篇論文中,我們先使用自殺字詞字典來篩選出可能被文章級別的注意力機制所忽略的高風險文章,並且對選出的高風險文章運用文字級別的模型來找回因為特徵提取的文章向量所失去的資訊。我們也證明了先前研究所採取的FScore可以簡化成Accuracy的函數,無法真實反映模型在資料不平衡情況下的效能。在本研究中,我們額外採取macro F1 Score作為評價指標。實驗結果顯示,我們提出的方法優於先前的研究,加了文字級別的模型不僅在FScore表現上優於先前的研究,更可以提升macro F1 Score近4個百分點,達到42.3%。
Suicide is a serious issue around the world. According to the US Centers for Disease Control and Prevention, an estimated 12.2 million adults seriously thought about suicide, 3.2 million made a plan, and 1.2 million attempted suicide. With the rapid development of information technology, and the good anonymity of social media, more and more people begin to use social media to share their inner feelings. It enables social media data to be widely used for research on suicide risk assessment. However, not all social media posts are suicide related. Even a person with a high suicide risk may have only a few posts containing suicide-related risk signals. Previous research addressed this problem with post-level attention mechanism. However, post-level attention mechanism may not find the correct suicide posts. This problem becomes more serious in the feature-based post embeddings since posts have been converted to post embeddings before the training phase of the model and the word information has been lost.
In this thesis, we use a suicide keyword dictionary to select risky posts which may be lost by the post-level attention mechanism and then build a word-level model for the risky posts to get back the lost information in the feature-based post embeddings. We also demonstrate that the FScore used in previous studies can be reduced to the function of accuracy, which does not reflect the model performance in predicting imbalanced datasets. Therefore, we additionally adopt macro F1 score as the evaluation function. Experiment results show that our model not only outperforms previous studies in FScore performance, but also achieves macro F1 Score of 42.3%, a nearly 4% improvement compared to previous studies.
摘要 i
Abstract ii
Acknowledgement iii
Table of Contents iv
List of Figures v
List of Tables vi
1. Introduction 1
2. Related Work 5
2.1 Suicide Risk Detection on Social Media 5
2.2 NLP for Suicide Risk Severity 6
3. Dataset 8
4. Method 10
4.1 Post-Level Model 11
4.2 Word-Level Model 12
4.2.1 Post Selection Layer 12
4.2.2 Word Embeddings 15
4.2.3 Self-Attention Layer 15
4.3 The PLWL Model 16
5. Experiments 17
5.1 Experimental Setup 17
5.1.1 Experiment Settings 17
5.1.2 Evaluation Metrics 18
5.1.3 Preprocessing 20
5.2 Results and Analysis 20
5.3 Ablation Studies 22
5.3.1 PLWL with One-Stage Training 22
5.3.2 Different Oversampling Proportions 23
5.3.3 Filtering out Users without Risky Posts 24
5.3.4 Different Embeddings 25
5.4 Case Studies 27
6. Conclusion and Future Work 30
Reference 31
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