探索門控圖神經網路於心理諮詢文字情感強度預測

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姓名

張容瑛(Jung-Ying Chang) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

探索門控圖神經網路於心理諮詢文字情感強度預測
(Exploring Gated Graph Neural Networks for Sentiment Intensity Prediction of Psychological Counseling Texts)

相關論文

★ 上下文嵌入增強異質圖注意力網路模型於心理諮詢文本多標籤分類

★ 基於階層式聚類注意力之編碼解碼器於醫療問題多答案摘要

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至系統瀏覽論文 (2029-7-23以後開放)

摘要(中)

維度型情感分析任務的目標是從輸入文本中，分析出作者的情緒?負面程度 (Valence) 以及激動程度 (Arousal)，可以應用於眾多情境中，例如:在心理諮詢和臨床心理學領域，幫助心理師和臨床醫生更好地了解患者的情感狀態和心理需求，進而提供更加準確且有效的心理輔導和治療。本研究旨在探討中文心理諮詢領域的維度型情感分析。我們提出了一個情感強化門控圖神經網路模型 (Sentiment-enhanced Gated Graph Neural Networks, SentiGGNN)，用於分析中文心理諮詢文本的情感強度與激動程度。首先，我們為每一篇輸入文本建構依存句法分析圖以及情感關聯圖。然後，經由門控圖神經網路來學習圖的節點表示。接著，經由雙向長短期記憶-卷積神經網路學習序列的表示，再透過注意力機制將節點表示與序列表示融合得到一個新的文本表示向量。最後，經過多層感知器得到文本的維度情感 (Valence-Arousal)預測值。
我們蒐集線上心理諮詢的民眾留言共 4,163 筆，然後人工標記維度情感取平均值，最終建置了第一個中文心理諮詢領域的維度型情感分析資料集 (Psycho-VASentiment)。藉由實驗與效能評估分析得知，我們提出的SentiGGNN模型優於其他相關研究模型 (包含 RNN, CNN, LSTM, Attention LSTM, Regional CNN-LSTM, Word-level BERT, HyperGAT, TextGCN, ADGCN, UGformer 以及 TextING)。?外，我們將維度型情感分析技術應用在社群媒體輿情分析，藉由案例分析從大數據下找到有用的觀點，藉以驗證維度情感分析技術的實務價值。

摘要(英)

Dimensional sentiment analysis focuses on predicting sentiment intensity in the valence arousal domains, which can be applied to help psychologists and clinicians understand the emotional state and psychological needs of patients, thereby providing more accurate and effective psychological counseling and therapy. This study aims to explore dimensional sentiment analysis in Chinese psychological counseling texts. We propose a Sentiment- enhanced Gated Graph Neural Networks (SentiGGNN) model to analyze sentiment intensities in the valence and arousal domains. Firstly, we construct sentiment and dependency graphs for each input text. Then, we learn node representations through GGNN architecture and sequence representations using BiLSTM-CNN networks. Subsequently, we fuse node representations with sequence representations based on the attention mechanism. Finally, we obtain the valence and arousal prediction values through a multi-layer perceptron.
We collected 4,163 online psychological counseling texts and manually annotated them to obtain average valence-arousal values, resulting in the first Chinese dimensional sentient analysis dataset in the psychological counseling domain, Psycho-VASentiment. Experimental results and performance evaluations revealed that our proposed SentiGGNN model performed other related methods, including RNN, CNN, LSTM, Attention LSTM, Regional CNN-LSTM, Word-level BERT, HyperGAT, TextGCN, ADGCN, UGformer, and TextING. In addition, we apply our dimensional sentiment analysis techniques to implement a social media analysis platform, providing valuable insights into the collected big data and confirming the effectiveness of our proposed model.

關鍵字(中)

★ 情緒分析
★ 情感運算
★ 圖神經網路
★ 注意力機制
★ 社群媒體分析

關鍵字(英)

★ Sentiment Analysis
★ Affective Computing
★ Graph Neural Networks
★ Attention Mechanism
★ Social Media Analysis

論文目次

摘要 i
ABSTRACT ii
誌謝 iii
目錄 iv
表目錄 vi
圖目錄 vii
第一章緒論 1
1-1研究背景 1
1-2研究動機與目的 3
1-3章節概要 5
第二章相關研究 6
2-1維度型情感語言資源 6
2-2 維度型情感分析模型 13
2-3 圖神經網路 26
第三章研究方法 30
3-1模型架構 30
3-2圖建構 32
3-3 門控圖神經網路層 36
3-4 雙向長短期記憶-卷積神經網路 38
3-5 注意力層 41
第四章實驗與效能評估 43
4-1 資料集建置 43
4-2 評估指標 49
4-4 實驗設定 50
4-5 模型比較 52
4-6 消融實驗 57
4-7嵌入向量分析 61
4-8 效能分析 62
4-9 展示系統 64
第五章輿情分析系統 65
5-1資料收集 65
5-2系統設計 68
第六章結論與未來工作 73
參考文獻 74
附錄 81

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指導教授

徐國鎧李龍豪(Kuo-Kai Shyu Lung-Hao Lee)

審核日期

2024-7-26

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