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題名 基於語意框架之讀者情緒偵測研究
Semantic Frame-based Approach for Reader-Emotion Detection作者 陳聖傑
Chen, Cen Chieh貢獻者 許聞廉<br>劉昭麟
Hsu, Wen Lian<br>Liu, Chao Lin
陳聖傑
Chen, Cen Chieh關鍵詞 情緒分析
讀者情緒偵測
文件分類
語意框架
Reader Emotion Detection
Semantic Frame
Frame-based Approach
Text classification
Sentiment Analysis日期 2016 上傳時間 3-二月-2016 12:14:03 (UTC+8) 摘要 過往對於情緒分析的研究顯少聚焦在讀者情緒，往往著眼於筆者情緒之研究。讀者情緒是指讀者閱讀文章後產生之情緒感受。然而相同一篇文章可能會引起讀者多種情緒反應，甚至產生與筆者迥異之情緒感受，也突顯其讀者情緒分析存在更複雜的問題。本研究之目的在於辨識讀者閱讀文章後之切確情緒，而文件分類的方法能有效地應用於讀者情緒偵測的研究，除了能辨識出正確的讀者情緒之外，並且能保留讀者情緒文件之相關內容。然而，目前的資訊檢索系統仍缺乏對隱含情緒之文件有效的辨識能力，特別是對於讀者情緒的辨識。除此之外，基於機器學習的方法難以讓人類理解，也很難查明辨識失敗的原因，進而無法了解何種文章引發讀者切確的情緒感受。有鑑於此，本研究提出一套基於語意框架(frame-based approach, FBA)之讀者情緒偵測研究的方法，FBA能模擬人類閱讀文章的方式外，並且可以有效地建構讀者情緒之基礎知識，以形成讀者情緒的知識庫。FBA具備高自動化抽取語意概念的基礎知識，除了利用語法結構的特徵，我們進一步考量周邊語境和語義關聯，將相似的知識整合成具有鑑別力之語意框架，並且透過序列比對(sequence alignment)的方式進行讀者情緒文件之匹配。經實驗結果顯示證明，本研究方法能有效地運用於讀者情緒偵測之相關研究。
Previous studies on emotion classification mainly focus on the writer`s emotional state. By contrast, this research emphasizes emotion detection from the readers` perspective. The classification of documents into reader-emotion categories can be applied in several ways, and one of the applications is to retain only the documents that cause desired emotions for enabling users to retrieve documents that contain relevant contents and at the same time instill proper emotions. However, current IR systems lack of ability to discern emotion within texts, reader-emotion has yet to achieve comparable performance. Moreover, the pervious machine learning-based approaches are generally not human understandable, thereby, it is difficult to pinpoint the reason for recognition failures and understand what emotions do articles trigger in their readers. We propose a flexible semantic frame-based approach (FBA) for reader`s emotion detection that simulates such process in human perception. FBA is a highly automated process that incorporates various knowledge sources to learn semantic frames that characterize an emotion and is comprehensible for humans from raw text. Generated frames are adopted to predict readers` emotion through an alignment-based matching algorithm that allows a semantic frame to be partially matched through a statistical scoring scheme. Experiment results demonstrate that our approach can effectively detect readers` emotion by exploiting the syntactic structures and semantic associations in the context as well as outperforms currently well-known statistical text classification methods and the stat-of-the-art reader-emotion detection method.參考文獻 [1] A. Bechara and A. R. Damasio, “The somatic marker hypothesis: A neural theory of economic decision”, Games and Economic Behavior, 52, 2, pp. 336-372, 2005.[2] B. Pang, & L. Lee, “A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts”. Proceedings of the ACL, Barcelona, Spain, Main Volume, pp. 271–278, 2004.[3] D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent dirichlet allocation”. Journal of Machine Learning Research, 3, pp. 993-1022, 2003.[4] B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs up? Sentiment Classification using Machine Learning Techniques,” Association for Computational Linguistics. 2002.[5] C. Cartes and V. Vapnik, “Support-Vector Networks,” Machine Learning, 20, pp. 273-297, 1995.[6] C. D. Manning and H. Schutze, “Foundations of statistical natural language processing,” volume 999. MIT Press, 1999.[7] C. J. C. Burges, “A Tutorial on Support Vector Machines for Pattern Recognition,” Data Mining and Knowledge Discovery. 2(2), pp. 1-47, 1998.[8] C. Cardie, J. Wiebe, T. Wilson and D. Litman, “Low-Level and Summary Representations of Opinions for Multi-Perspective Question Answering,” AAAI Spring Symposium on New Directions in Question Answering, 2003[9] W. Chai and B. Vercoe, “Folk music classification using hidden Markov models,” In Proceedings of the international conference on artificial intelligence, 2001.[10] C. H. Yang, K. H. Y. Lin, and H. H. Chen, “Writer meets reader: Emotion analysis of social media from both the writer’s and reader’s perspectives,” In Proceedings of the IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, Vol. 01, pp. 287– 290, 2009.[11] C. H. Wu, Z. J. Chuang, and Y. C. Lin, “Emotion recognition from text using semantic labels and separable mixture models,” ACM Transactions on Asian Language Information Processing (TALIP), vol. 5, issue 2, pp. 165-183, 2006.[12] J. M. Wiebe, “Learning Subjective Adjectives from Corpora,” In Proceedings of 17th Conference of the American Association for Artificial Intelligence, pp. 735-740. AAAI, 2000.[13] K. Chen, S. Huang, Y. Shih, and Y. Chen, "Multi-level Definitions and Complex Relations in Extended-HowNet," Workshop on Chinese Lexical Semantics, 2004.[14] H. Kanayama and T. Nasukawa, “Fully Automatic Lexicon Expansion for Domain-Oriented Sentiment Analysis,” In Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2006.[15] K. H. Y. Lin, C. H. Yang, and H. H. Chen. “What emotions do news articles trigger in their readers?,” In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 733–734. ACM, 2006.[16] K. Dave, S. Lawrence, and D. M. Pennock, “Mining the peanut gallery: Opinion extraction and semantic classification of product reviews,” WWW2003, 2003.[17] K. H. Lin, C. H. Yang, and H. H. Chen, “Emotion Classification of Online News Articles from the Reader’s Perspective,” In Proceedings of International Conference on Web Intelligence, pp. 220- 226. 2008.[18] A. McCallum and, K. Nigam, “A comparison of event models for Naïve Bayes text classification,” In Proceedings of AAAI/ICML-98 Workshop on Learning for Text Categorization, 41-48. 1998.[19] M. R. Garey and D. S. Johnson, “Computers and intractability: A Guide to the Theory of NP- Completeness”. Freeman San Francisco. 1979.[20] S. Morinaga, K. Yamanishi., K. Tateishi, and T. Fukushima, “Mining Product Reputations on the Web”. KDD’02. 2002.[21] S. M. Kim and E. Hovy, “Determining the Sentiment of Opinions,” In Proc. of 20th International Conference on Computational Linguistics. ACL, Geneva, CH, 2004[22] S. B. Needleman and C. D. Wunsch, “A general method applicable to the search for similarities in the amino acid sequence of two proteins,” Journal of molecular biology, 48(3):443–453. 1970.[23] S. Bethard, H. Yu, A. Thornton, V. Hatzivassiloglou, and D. Jurafsky, “Automatic extraction of opinion propositions and their holders,” In Working Notes of the AAAI Spring Symposium on Exploring Attitude and Affect in Text: Theories and Applications, 2004.[24] S. Guha and S. Khuller, “Approximation algorithms for connected dominating sets,” Algorithmica, 20(4):374–387, 1998.[25] T. Mullen and N. Collier. “Sentiment Analysis Using Support Vector Machines with Diverse Information Sources,” In Proceeding of Conference on Empirical Methods in Natural Language Processing. ACL, Barcelon, ES, 2004.[26] V. Hatzivassiloglou and K. R. McKeown. “Predicting the semantic orientation of adjectives,” In ACL97, 1997.[27] W. L. Hsu, Y. S. Chen, and Y. K. Wang, “A context sensitive model for concept understanding,” In Proceeding of 3rd International Conference on Information Theoretic Approaches to Logic, Language, and Computation, 1998.[28] S. S. Wilks, “The Likelihood Test of Independence in Contingency Tables,” Ann. Math. Statist. 6, no. 4, 190--196. doi:10.1214/aoms/1177732564. 1935. [29] Y. Hu, J. Duan, X. Chen, B. Pei, and R. Lu. “A New Method for Sentiment Classification in Text Retrieval,” In Proceeding of 2nd International Joint Conference on Natural Language Processing, 1-9. Jeju Island, KR, 2005[30] C. H. Yang, K. H. Y. Lin and H. H. Chen, “Building emotion lexicon from Weblog corpora,” In Proceedings of 45th Annual Meeting of Association for Computational Linguistics, poster, 2007.[31] Y. Hu, J. Duan, X. Chen, B. Pei, and R. Lu, “A New Method for Sentiment Classification in Text Retrieval,” In Proceedings of 2nd International Joint Conference on Natural Language Processing, 1-9. Jeju Island, KR. 2005.[32] Y. H. Yang, C. C. Liu, and H. H. Chen, “Music emotion classification: A fuzzy approach,” In Proceedings of the 14th Annual ACM International Conference on Multimedia, MULTIMEDIA, pages 81–84, 2006.[33] Z. Kovecses, “Language and emotion concepts. In Metaphor and Emotion: Language, Culture, and Body in Human Feeling,” Cambridge: Cambridge University Press, 2003.[34] Z. D. Dong, Q. Dong, and C. L. Hao. “Hownet and its computation of meaning,” In Proceedings of the 23rd International Conference on Computational Linguistics: Demonstrations, pages 53–56. Association for Computational Linguistics, 2010.[35] F. Zou, F. L. Wang, X. Deng, S. Han, and L. S. Wang, “Automatic construction of Chinese stop word list,” In Proceedings of the 5th WSEAS International Conference on Applied Computer Science, pp. 1010-1015, 2006.[36] Y. J. Tang and H. H. Chen. “Mining sentiment words from microblogs for predicting writer-reader emotion transition,” In LREC, pp. 1226–1229, 2012.[37] S. Scott and S. Matwin, “Feature engineering for text classification,” In Proceeding of the 16th International Conference on Machine Learning, pp. 379-388, 1999.[38] B. Mirkin, “Mathematical Classifcation and Clustering”. Kluwer, 1996. [39] Y. M. Yang, “An evaluation of statistical approaches to text categorization,” Journal of Information Retrieval, 1999. 描述 碩士
國立政治大學
資訊科學學系
102753001資料來源 http://thesis.lib.nccu.edu.tw/record/#G0102753001 資料類型 thesis dc.contributor.advisor 許聞廉<br>劉昭麟 zh_TW dc.contributor.advisor Hsu, Wen Lian<br>Liu, Chao Lin en_US dc.contributor.author (作者) 陳聖傑 zh_TW dc.contributor.author (作者) Chen, Cen Chieh en_US dc.creator (作者) 陳聖傑 zh_TW dc.creator (作者) Chen, Cen Chieh en_US dc.date (日期) 2016 en_US dc.date.accessioned 3-二月-2016 12:14:03 (UTC+8) - dc.date.available 3-二月-2016 12:14:03 (UTC+8) - dc.date.issued (上傳時間) 3-二月-2016 12:14:03 (UTC+8) - dc.identifier (其他 識別碼) G0102753001 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/81199 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊科學學系 zh_TW dc.description (描述) 102753001 zh_TW dc.description.abstract (摘要) 過往對於情緒分析的研究顯少聚焦在讀者情緒，往往著眼於筆者情緒之研究。讀者情緒是指讀者閱讀文章後產生之情緒感受。然而相同一篇文章可能會引起讀者多種情緒反應，甚至產生與筆者迥異之情緒感受，也突顯其讀者情緒分析存在更複雜的問題。本研究之目的在於辨識讀者閱讀文章後之切確情緒，而文件分類的方法能有效地應用於讀者情緒偵測的研究，除了能辨識出正確的讀者情緒之外，並且能保留讀者情緒文件之相關內容。然而，目前的資訊檢索系統仍缺乏對隱含情緒之文件有效的辨識能力，特別是對於讀者情緒的辨識。除此之外，基於機器學習的方法難以讓人類理解，也很難查明辨識失敗的原因，進而無法了解何種文章引發讀者切確的情緒感受。有鑑於此，本研究提出一套基於語意框架(frame-based approach, FBA)之讀者情緒偵測研究的方法，FBA能模擬人類閱讀文章的方式外，並且可以有效地建構讀者情緒之基礎知識，以形成讀者情緒的知識庫。FBA具備高自動化抽取語意概念的基礎知識，除了利用語法結構的特徵，我們進一步考量周邊語境和語義關聯，將相似的知識整合成具有鑑別力之語意框架，並且透過序列比對(sequence alignment)的方式進行讀者情緒文件之匹配。經實驗結果顯示證明，本研究方法能有效地運用於讀者情緒偵測之相關研究。 zh_TW dc.description.abstract (摘要) Previous studies on emotion classification mainly focus on the writer`s emotional state. By contrast, this research emphasizes emotion detection from the readers` perspective. The classification of documents into reader-emotion categories can be applied in several ways, and one of the applications is to retain only the documents that cause desired emotions for enabling users to retrieve documents that contain relevant contents and at the same time instill proper emotions. However, current IR systems lack of ability to discern emotion within texts, reader-emotion has yet to achieve comparable performance. Moreover, the pervious machine learning-based approaches are generally not human understandable, thereby, it is difficult to pinpoint the reason for recognition failures and understand what emotions do articles trigger in their readers. We propose a flexible semantic frame-based approach (FBA) for reader`s emotion detection that simulates such process in human perception. FBA is a highly automated process that incorporates various knowledge sources to learn semantic frames that characterize an emotion and is comprehensible for humans from raw text. Generated frames are adopted to predict readers` emotion through an alignment-based matching algorithm that allows a semantic frame to be partially matched through a statistical scoring scheme. Experiment results demonstrate that our approach can effectively detect readers` emotion by exploiting the syntactic structures and semantic associations in the context as well as outperforms currently well-known statistical text classification methods and the stat-of-the-art reader-emotion detection method. en_US dc.description.tableofcontents 1 Introduction 11.1 Background 11.2 Text Classification 11.3 Problem Definition 31.4 Our Goal 41.5 Organization of this Dissertation 42 Related Work 53 System Architecture 83.1 Crucial Element Labeling (CEL) 93.1.1 Emotion Keyword (Keyword) 93.1.2 Named Entity Ontology (NEO) 103.1.3 Extended HowNet (E-HowNet) 133.2 Semantic Frame Generation(SFG) 153.3 Semantic Frame Matching(SFM) 184 Experiment 214.1 Experiment Setting 214.1.1 Datasets 214.1.2 Comparison Setting and Evaluation Metrics 224.2 Results and Discussion 235 Conclusion and Future Work 28References 30 zh_TW dc.format.extent 1343643 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0102753001 en_US dc.subject (關鍵詞) 情緒分析 zh_TW dc.subject (關鍵詞) 讀者情緒偵測 zh_TW dc.subject (關鍵詞) 文件分類 zh_TW dc.subject (關鍵詞) 語意框架 zh_TW dc.subject (關鍵詞) Reader Emotion Detection en_US dc.subject (關鍵詞) Semantic Frame en_US dc.subject (關鍵詞) Frame-based Approach en_US dc.subject (關鍵詞) Text classification en_US dc.subject (關鍵詞) Sentiment Analysis en_US dc.title (題名) 基於語意框架之讀者情緒偵測研究 zh_TW dc.title (題名) Semantic Frame-based Approach for Reader-Emotion Detection en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1] A. Bechara and A. R. Damasio, “The somatic marker hypothesis: A neural theory of economic decision”, Games and Economic Behavior, 52, 2, pp. 336-372, 2005.[2] B. Pang, & L. Lee, “A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts”. Proceedings of the ACL, Barcelona, Spain, Main Volume, pp. 271–278, 2004.[3] D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent dirichlet allocation”. Journal of Machine Learning Research, 3, pp. 993-1022, 2003.[4] B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs up? Sentiment Classification using Machine Learning Techniques,” Association for Computational Linguistics. 2002.[5] C. Cartes and V. Vapnik, “Support-Vector Networks,” Machine Learning, 20, pp. 273-297, 1995.[6] C. D. Manning and H. Schutze, “Foundations of statistical natural language processing,” volume 999. MIT Press, 1999.[7] C. J. C. Burges, “A Tutorial on Support Vector Machines for Pattern Recognition,” Data Mining and Knowledge Discovery. 2(2), pp. 1-47, 1998.[8] C. Cardie, J. Wiebe, T. Wilson and D. Litman, “Low-Level and Summary Representations of Opinions for Multi-Perspective Question Answering,” AAAI Spring Symposium on New Directions in Question Answering, 2003[9] W. Chai and B. Vercoe, “Folk music classification using hidden Markov models,” In Proceedings of the international conference on artificial intelligence, 2001.[10] C. H. Yang, K. H. Y. Lin, and H. H. Chen, “Writer meets reader: Emotion analysis of social media from both the writer’s and reader’s perspectives,” In Proceedings of the IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, Vol. 01, pp. 287– 290, 2009.[11] C. H. Wu, Z. J. Chuang, and Y. C. Lin, “Emotion recognition from text using semantic labels and separable mixture models,” ACM Transactions on Asian Language Information Processing (TALIP), vol. 5, issue 2, pp. 165-183, 2006.[12] J. M. Wiebe, “Learning Subjective Adjectives from Corpora,” In Proceedings of 17th Conference of the American Association for Artificial Intelligence, pp. 735-740. AAAI, 2000.[13] K. Chen, S. Huang, Y. Shih, and Y. Chen, "Multi-level Definitions and Complex Relations in Extended-HowNet," Workshop on Chinese Lexical Semantics, 2004.[14] H. Kanayama and T. Nasukawa, “Fully Automatic Lexicon Expansion for Domain-Oriented Sentiment Analysis,” In Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2006.[15] K. H. Y. Lin, C. H. Yang, and H. H. Chen. “What emotions do news articles trigger in their readers?,” In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 733–734. ACM, 2006.[16] K. Dave, S. Lawrence, and D. M. Pennock, “Mining the peanut gallery: Opinion extraction and semantic classification of product reviews,” WWW2003, 2003.[17] K. H. Lin, C. H. Yang, and H. H. Chen, “Emotion Classification of Online News Articles from the Reader’s Perspective,” In Proceedings of International Conference on Web Intelligence, pp. 220- 226. 2008.[18] A. McCallum and, K. Nigam, “A comparison of event models for Naïve Bayes text classification,” In Proceedings of AAAI/ICML-98 Workshop on Learning for Text Categorization, 41-48. 1998.[19] M. R. Garey and D. S. Johnson, “Computers and intractability: A Guide to the Theory of NP- Completeness”. Freeman San Francisco. 1979.[20] S. Morinaga, K. Yamanishi., K. Tateishi, and T. Fukushima, “Mining Product Reputations on the Web”. KDD’02. 2002.[21] S. M. Kim and E. Hovy, “Determining the Sentiment of Opinions,” In Proc. of 20th International Conference on Computational Linguistics. ACL, Geneva, CH, 2004[22] S. B. Needleman and C. D. Wunsch, “A general method applicable to the search for similarities in the amino acid sequence of two proteins,” Journal of molecular biology, 48(3):443–453. 1970.[23] S. Bethard, H. Yu, A. Thornton, V. Hatzivassiloglou, and D. Jurafsky, “Automatic extraction of opinion propositions and their holders,” In Working Notes of the AAAI Spring Symposium on Exploring Attitude and Affect in Text: Theories and Applications, 2004.[24] S. Guha and S. Khuller, “Approximation algorithms for connected dominating sets,” Algorithmica, 20(4):374–387, 1998.[25] T. Mullen and N. Collier. “Sentiment Analysis Using Support Vector Machines with Diverse Information Sources,” In Proceeding of Conference on Empirical Methods in Natural Language Processing. ACL, Barcelon, ES, 2004.[26] V. Hatzivassiloglou and K. R. McKeown. “Predicting the semantic orientation of adjectives,” In ACL97, 1997.[27] W. L. Hsu, Y. S. Chen, and Y. K. Wang, “A context sensitive model for concept understanding,” In Proceeding of 3rd International Conference on Information Theoretic Approaches to Logic, Language, and Computation, 1998.[28] S. S. Wilks, “The Likelihood Test of Independence in Contingency Tables,” Ann. Math. Statist. 6, no. 4, 190--196. doi:10.1214/aoms/1177732564. 1935. [29] Y. Hu, J. Duan, X. Chen, B. Pei, and R. Lu. “A New Method for Sentiment Classification in Text Retrieval,” In Proceeding of 2nd International Joint Conference on Natural Language Processing, 1-9. Jeju Island, KR, 2005[30] C. H. Yang, K. H. Y. Lin and H. H. Chen, “Building emotion lexicon from Weblog corpora,” In Proceedings of 45th Annual Meeting of Association for Computational Linguistics, poster, 2007.[31] Y. Hu, J. Duan, X. Chen, B. Pei, and R. Lu, “A New Method for Sentiment Classification in Text Retrieval,” In Proceedings of 2nd International Joint Conference on Natural Language Processing, 1-9. Jeju Island, KR. 2005.[32] Y. H. Yang, C. C. Liu, and H. H. Chen, “Music emotion classification: A fuzzy approach,” In Proceedings of the 14th Annual ACM International Conference on Multimedia, MULTIMEDIA, pages 81–84, 2006.[33] Z. Kovecses, “Language and emotion concepts. In Metaphor and Emotion: Language, Culture, and Body in Human Feeling,” Cambridge: Cambridge University Press, 2003.[34] Z. D. Dong, Q. Dong, and C. L. Hao. “Hownet and its computation of meaning,” In Proceedings of the 23rd International Conference on Computational Linguistics: Demonstrations, pages 53–56. Association for Computational Linguistics, 2010.[35] F. Zou, F. L. Wang, X. Deng, S. Han, and L. S. Wang, “Automatic construction of Chinese stop word list,” In Proceedings of the 5th WSEAS International Conference on Applied Computer Science, pp. 1010-1015, 2006.[36] Y. J. Tang and H. H. Chen. “Mining sentiment words from microblogs for predicting writer-reader emotion transition,” In LREC, pp. 1226–1229, 2012.[37] S. Scott and S. Matwin, “Feature engineering for text classification,” In Proceeding of the 16th International Conference on Machine Learning, pp. 379-388, 1999.[38] B. Mirkin, “Mathematical Classifcation and Clustering”. Kluwer, 1996. [39] Y. M. Yang, “An evaluation of statistical approaches to text categorization,” Journal of Information Retrieval, 1999. zh_TW
