[1] Lutz C, White G M. The anthropology of emotions. Annual Review of Anthropology, 1986, 15: 405-436
[2] Wei Nai-Xing. Fundamentals of Lexis. Shanghai: Shanghai Foreign Language Education Press, 2011.(卫乃兴. 词语学要义. 上海: 上海外语教育出版社, 2011.)
[3] Alm C O, Roth D, Sproat R. Emotions from text: machine learning for text-based emotion prediction. In: Proceedings of the 2005 Conference on Human Language Technology and Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2005. 579-586
[4] Aman S, Szpakowicz S. Using roget's thesaurus for fine-grained emotion recognition. In: Proceedings of the 3rd International Joint Conference on Natural Language Processing. Hyderabad, India: Researchgate, 2008. 312-318
[5] Das D, Bandyopadhyay S. Word to sentence level emotion tagging for Bengali blogs. In: Proceedings of the 47th Annual Meeting of the Association for Computational Linguistics and the 4th International Joint Conference on Natural Language Processing of the AFNLP. Stroudsburg, PA, USA: Association for Computational Linguistics, 2009. 149-152
[6] Nakagawa T, Inui K, Kurohashi S. Dependency tree-based sentiment classification using CRFs with hidden variables. In: Proceedings of Human Language Technologies: the 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2010. 786-794
[7] Xu Bing, Zhao Tie-Jun, Wang Shan-Yu, Zheng De-Quan. Extraction of opinion targets based on shallow parsing features. Acta Automatica Sinica, 2011, 37(10): 1241-1247(徐冰, 赵铁军, 王山雨, 郑德权. 基于浅层句法特征的评价对象抽取研究. 自动化学报, 2011, 37(10): 1241-1247)
[8] Neviarouskaya A, Prendinger H, Ishizuka M. Compositionality principle in recognition of fine-grained emotions from text. In: Proceedings of International Conference on Weblogs and Social Media. San Jose, California: The AAAI Press, 2009. 6-24
[9] Neviarouskaya A, Prendinger H, Ishizuka M. Recognition of affect, judgment, and appreciation in text. In: Proceedings of the 23rd International Conference on Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2010. 806-814
[10] Chaffar S, Inkpen D. Using a heterogeneous dataset for emotion analysis in text. In: Proceedings of the Canadian AI'11 Proceedings of the 24th Canadian conference on Advances in Artificial Intelligence. St. John's, Canada: Springer, 2011. 62-67
[11] Mohammad S, Turney P D. Emotions evoked by common words and phrases: using mechanical Turk to create an emotion lexicon. In: Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text. Stroudsburg, PA, USA: Association for Computational Linguistics, 2010. 26-34
[12] Volkova S, Dolan W B, Wilson T. CLex: a lexicon for exploring color, concept and emotion associations in language. In: Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2012. 306-314
[13] Purver M, Battersby S. Experimenting with distant supervision for emotion classification. In: Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2012. 482-491
[14] Qadir A, Riloff E. Bootstrapped learning of emotion hashtags #hashtags4you. In: Proceeding of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. Stroudsburg, PA, USA: Association for Computational Linguistics, 2013. 2-11
[15] Quan C Q, Ren F J. Construction of a blog emotion corpus for Chinese emotional expression analysis. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2009. 1446-1454
[16] Xu G, Meng X F, Wang H F. Build Chinese emotion lexicons using a graph-based algorithm and multiple resources. In: Proceedings of the 23rd International Conference on Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2010. 1209-1217
[17] Wang L, Yu S W. Construction of a Chinese idiom knowledge base and its applications. In: Proceedings of the Multiword Expressions: From Theory to Applications (MWE 2010). Beijing, China: Association for Computational Linguistics, 2010. 11-18
[18] Yang M, Peng B L, Chen Z, Zhu D J, Chow K P. A topic model for building fine-grained domain-specific emotion lexicon. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2014. 421-426
[19] Choi Y, Cardie C. Learning with compositional semantics as structural inference for subsentential sentiment analysis. In: Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2008. 793-801
[20] Mitchell J, Lapata M. Vector-based models of semantic composition. In: Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2008. 236-244
[21] Baroni M, Zamparelli R. Nouns are vectors, adjectives are matrices: representing adjective-noun constructions in semantic space. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2010. 1183-1193
[22] Socher R, Pennington J, Huang E H, Ng A Y, Manning C D. Semi-supervised recursive autoencoders for predicting sentiment distributions. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2011. 151-161
[23] Socher R, Huval B, Manning C D, Ng A Y. Semantic compositionality through recursive matrix-vector spaces. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Stroudsburg, PA, USA: Association for Computational Linguistics, 2012. 1201-1211
[24] Moilanen K, Pulman S. Sentiment composition. In: Proceedings of Recent Advances in Natural Language Processing. Borovets, Bulgaria: University of Oxford, 2007. 378-382
[25] Yessenalina A, Cardie C. Compositional matrix-space models for sentiment analysis. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2011. 172-182
[26] Mohammad S. Portable features for classifying emotional text. In: Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg, PA, USA: Association for Computational Linguistics, 2012. 587-591
[27] Hermann K M, Blunsom P. The role of syntax in vector space models of compositional semantics. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2013. 894-904
[28] Sui Zhi-Fang, Yu Shi-Wen. The research on recognizing the predicate head of a Chinese simple sentence in EBMT. Journal of Chinese Information Processing, 1998, 12(4): 39-46(穗志方, 俞士汶. 面向EBMT的汉语单句谓语中心词识别研究. 中文信息学报, 1998, 12(4): 39-46)
[29] Turian J, Ratinov L, Bengio Y. Word representations: a simple and general method for semi-supervised learning. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2010. 384-394
[30] Sarawagi S, Cohen W W. Semi-Markov conditional random fields for information extraction. In: Proceedings of the Eighteenth Annual Conference on Neural Information Processing Systems. Montreal, Quebec, Canada, 2004.