探索門控圖神經網路於心理諮詢文字情感強度預測

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：81

、訪客IP：3.142.252.185

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

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

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

參考文獻

Stefano Baccianella, Andrea Esuli, and Fabrizio Sebastiani. 2010. Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In Proceedings of the Seventh International Conference on Language Resources and Evaluation, pp. 2200-2204.
Alena Neviarouskaya, Helmut Prendinger, and Mitsuru Ishizuka. 2011. SentiFul: A lexicon for sentiment analysis, IEEE Transactions on Affective Computing, no. 2, pp. 22-36.
Clayton Hutto and Eric Gilbert. 2014. Vader: A parsimonious rule-based model for sentiment analysis of social media text. In Proceedings of the international AAAI Conference on Web and Social Media, vol. 8, no. 1, pp. 216-225.
Erik Cambria, Yang Li, Frank Z Xing, Soujanya Poria, and Kenneth Kwok. 2020. SenticNet 6: Ensemble application of symbolic and subsymbolic AI for sentiment analysis. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management, pp. 105-114.
Sara Rosenthal, Preslav Nakov, Svetlana Kiritchenko, Saif Mohammad, Alan Ritter, and Veselin Stoyanov. 2015. SemEval-2015 task 10: Sentiment analysis in Twitter. In Proceedings of the 9th International Workshop on Semantic Evaluation, pp. 451-463.
Svetlana Kiritchenko, Saif Mohammad, and Mohammad Salameh. 2016. Semeval-2016 task 7: Determining sentiment intensity of english and arabic phrases. In Proceedings of the 10th International Workshop on Semantic Evaluation, pp. 42-51.
Keith Cortis, André Freitas, Tobias Daudert, Manuela Huerlimann, Manel Zarrouk, Siegfried Handschuh, and Brian Davis. 2017. Semeval-2017 task 5: Fine-grained sentiment analysis on financial microblogs and news. In Proceedings of the 11th International Workshop on Semantic Evaluation, pp. 519-535.
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts. 2013. Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1631-1642.
Dan Klein and Christopher D. Manning. 2003. Accurate Unlexicalized Parsing. In Proceedings of the 41st Annual Meeting of the Association for Computational Linguistics, pp. 423-430.
Margaret M Bradley and Peter J. Lang. 1999. Affective Norms for English Words (ANEW): Instruction Manual and Affective Ratings. Technical Report C-1, the Center for Research in Psychophysiology, University of Florida.
Amy Beth Warriner, Victor Kuperman, and Marc Brysbaert. 2013. Norms of valence, arousal, and dominance for 13,915 English lemmas. Behavior Research Methods, vol. 45, pp. 1191-1207.
Margaret M Bradley and Peter J Lang. 2007. Affective Norms for English Text (ANET): Affective ratings of text and instruction manual, Techical Report. D-1, University of Florida, Gainesville, FL.
Daniel Preoţiuc-Pietro, H Andrew Schwartz, Gregory Park, Johannes Eichstaedt, Margaret Kern, Lyle Ungar, and Elisabeth Shulman. 2016. Modelling valence and arousal in facebook posts. In Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 9-15.
Sven Buechel and Udo Hahn. 2017. Emobank: Studying the impact of annotation perspective and representation format on dimensional emotion analysis. arXiv preprint arXiv:2205.01996.
Liang-Chih Yu, Lung-Hao Lee, and Kam-Fai Wong. 2016b. Overview of the IALP 2016 shared task on dimensional sentiment analysis for Chinese words, In 2016 International Conference on Asian Language Processing, pp. 156-160.
Liang-Chih Yu, Lung-Hao Lee, Jin Wang, and Kam-Fai Wong. 2017. IJCNLP-2017 Task 2: Dimensional sentiment analysis for Chinese phrases, In Proceedings of the 17th International Joint Conference on Natural Language Processing, pp. 9-16.
Liang-Chih Yu, Jin Wang, Bo Peng, and Chu-Ren Huang. 2021. ROCLING-2021 Shared Task: Dimensional Sentiment Analysis for Educational Texts. In Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing, pp. 385-388.
Lung-Hao Lee, Jian-Hong Li, and Liang-Chih Yu. 2022. Chinese EmoBank: Building valence-arousal resources for dimensional sentiment analysis. ACM Transactions on Asian and Low-Resource Language Information Processing, vol. 21, no. 4, pp. 1-18.
Chin-Lan Huang, Cindy K Chung, Natalie Hui, Yi-Cheng Lin, Yi-Tai Seih, Ben CP Lam, Wei-Chuan Chen, Michael H Bond, and James W Pennebaker. 2012. The development of the Chinese linguistic inquiry and word count dictionary. Chinese Journal of Psychology, vol. 54, no. 2, pp. 185-201.
Lun‐Wei Ku, and Hsin‐Hsi Chen. 2007. Mining opinions from the Web: Beyond relevance retrieval. Journal of the American Society for Information Science and Technology, vol. 58, no. 12, pp. 1838-1850.
Peter D. Turney and Michael L. Littman. 2002. Unsupervised learning of semantic orientation from a hundred-billion-word corpus. arXiv preprint, arXiv:cs/0212012.
Liang-Chih Yu, Jin Wang, K. Robert Lai, and Xue-jie Zhang. 2015. Predicting valence-arousal ratings of words using a weighted graph method. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, vol. 2, pp. 788-793.
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient Estimation of Word Representations in Vector Space. arXiv preprint, arXiv:1301.3781.
Livia Polanyi and Annie Zaenen. 2006. Contextual valence shifters. Computing Attitude and Affect in Text: Theory and Applications, pp. 1-10.
Liang-Chih Yu, Jin Wang, K. Robert Lai, and Xuejie Zhang. 2020. Pipelined Neural Networks for Phrase-Level Sentiment Intensity Prediction. IEEE Transactions on Affective Computing, vol. 11, no. 03, pp. 447-458.
Yu-Chih Deng, Cheng-Yu Tsai, Yih-Ru Wang, Sin-Horng Chen, and Lung-Hao Lee. 2022. Predicting Chinese phrase-level sentiment intensity in valence-arousal dimensions with linguistic dependency features, IEEE Access, vol. 10, pp. 126612-126620.
Jingjing Liu and Stephanie Seneff. 2009. Review Sentiment Scoring via a Parse-and-Paraphrase Paradigm. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, pp. 161-169.
Georgios Paltoglou, Mathias Theunis, Arvid Kappas, and Mike Thelwall. 2013. Predicting Emotional Responses to Long Informal Text. IEEE Transactions on Affective Computing, vol. 4, no. 1, pp. 106-115.
Jin Wang, Liang-Chih Yu, K. Robert Lai, and Xuejie Zhang. 2016. Dimensional sentiment analysis using a regional CNN-LSTM model. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, vol. 2, pp. 225-230.
Yoshua Bengio, Patrice Simard, and Paolo Frasconi. 1994. Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks, vol. 5, no. 2, pp. 157-166.
Md Shad Akhtar, Abhishek Kumar, Deepanway Ghosal, Asif Ekbal, and Pushpak Bhattacharyya. 2017. A Multilayer Perceptron based Ensemble Technique for Fine-grained Financial Sentiment Analysis. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 540–546.
Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, and Pierre-Antoine Manzagol. 2010. Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion. Journal of Machine Learning Research, vol. 11, no. 12, pp. 3371-3408.
Xiaowen Ding, Bing Liu, and Philip S. Yu. 2008. A Holistic Lexicon-Based Approach to Opinion Mining. In Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 231-240.
Theresa Wilson, Janyce Wiebe, and Paul Hoffmann. 2005. Recognizing Contextual Polarity in Phrase- level Sentiment Analysis. In Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 347-354.
Jin Wang, Liang-Chih Yu, K. Robert Lai, and Xuejie Zhang. 2020. Tree-structured regional CNN-LSTM model for dimensional sentiment analysis. IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 28, pp. 581-591.
Amal Htait and Leif Azzopardi. 2021. Sentiment intensity prediction using neural word embeddings. Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval, pp. 93-102.
Yu-Chih Deng, Yih-Ru Wang, Sin-Horng Chen, and Lung-Hao Lee. 2023. Towards Transformer Fusions for Chínese Sentiment Intensity Prediction in Valence-Arousal Dimensions, IEEE Access, vol. 11, pp. 109974-109982.
Liang Yao Chengsheng Mao, and Yuan Luo. 2019. Graph convolutional networks for text classification. In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 1, pp. 7370-7377.
Yufeng Zhang, Xueli Yu, Zeyu Cui, Shu Wu, Zhongzhen Wen, and Liang Wang. 2020. Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 334-339.
Kaize Ding, Jianling Wang, Jundong Li, Dingcheng Li, and Huan Liu. 2020. Be More with Less: Hypergraph Attention Networks for Inductive Text Classification. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, pp. 4927-4936.
Chenwei Lou, Bin Liang, Lin Gui, Yulan He, Yixue Dang, and Ruifeng Xu. 2021. Affective dependency graph for sarcasm detection. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1844-1849.
Dai Quoc Nguyen, Tu Dinh Nguyen, and Dinh Phung. 2022. Universal graph transformer self-attention networks. In Companion Proceedings of the Web Conference 2022, pp. 193-196.
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel. 2015. Gated graph sequence neural networks. arXiv preprint, arXiv:1511.05493.
Chen Zhang, Qiuchi Li, and Dawei Song. 2019. Aspect-based sentiment classification with aspect-specific graph convolutional networks, In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pp. 4568-4578.
Margaret M Bradley and Peter J Lang. 1994. Measuring emotion: the self-assessment manikin and the semantic differential, Journal of Behavior Therapy and Experimental Psychiatry, vol. 25, pp. 49-59.
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014. GloVe: Global Vectors for Word Representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pp. 1532-1543.
Ozan Irsoy and Claire Cardie. 2014. Opinion mining with deep recurrent neural networks. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pp. 720-728.
Yoon Kim. 2014. Convolutional Neural Networks for Sentence Classification. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pp. 1746-1751.
Xin Wang, Yuanchao Liu, Chengjie Sun, Baoxun Wang, and Xiaolong Wang. 2015. Predicting polarities of tweets by composing word embeddings with long short-term memory. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, vol. 1, pp. 1343-1353.
Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy. 2016. Hierarchical Attention Networks for Document Classification. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1480-1489.
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint, arXiv:1810.04805.
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017. Enriching Word Vectors with Subword Information. Transactions of the Association for Computational Linguistics, vol. 5, pp. 135-146.

指導教授

徐國鎧李龍豪(Kuo-Kai Shyu Lung-Hao Lee)

審核日期

2024-7-26

推文