[1]
Plutchik R., Emotion: A Psychoevolutionary Synthesis. New York, NY, USA: Harpercollins College Division, 1980.
[2]
Ekman P., Expression and the nature of emotion, in Approaches to Emotion, Scherer K. and Ekman P., eds. Hillsdale, NJ, USA: Erlbaum, 1984, pp. 19-344.
[3]
Turner J., On the Origins of Human Emotions: A Sociological Inquiry into the Evolution of Human Affect. Stanford, CA, USA: Stanford University Press, 2000.
[4]
Gui L., Xu R. F., Lu Q., Du J. C., and Zhou Y., Negative transfer detection in transductive transfer learning, Int. J. Mach. Learn. Cybern., .
[5]
Beck D., Cohn T., and Specia L., Joint emotion analysis via multi-task Gaussian processes, in Proc. 2014 Conf. Empirical Methods in Natural Language Processing, Doha, Qatar, 2014, pp. 1798-1803.
[6]
Gao W., Li S. S., Lee S. Y. M., Zhou G. D., and Huang C. R., Joint learning on sentiment and emotion classification, in Proc. 22nd ACM Int. Conf. Information and Knowledge Management, San Francisco, CA, USA, 2013, pp. 1505-1508.
[7]
Chang Y. C., Chen C. C., Hsieh Y. L., Chen C. C., and Hsu W. L., Linguistic template extraction for recognizing reader-emotion and emotional resonance writing assistance, in Proc. 53rd Annual Meeting of the Association for Computational Linguistics, Beijing, China, 2015, pp. 775-780.
[8]
Das D. and Bandyopadhyay S., Finding emotion holder from Bengali blog texts—An unsupervised syntactic approach, in Proc. 24th Pacific Asia Conf. Language, Information and Computation, Waseda, Japan, 2010, pp. 621-628.
[9]
Gui L., Xu R. F., He Y. L., Lu Q., and Wei Z. Y., Intersubjectivity and sentiment: From language to knowledge, in Proc. 25th Int. Joint Conf. Artificial Intelligence, New York, NY, USA, 2016, pp. 2789-2795.
[10]
Gui L., Zhou Y., Xu R. F., He Y. L., and Lu Q., Learning representations from heterogeneous network for sentiment classification of product reviews, Knowl. Based Syst., vol. 124, pp. 34-45, 2017.
[11]
Xie H. R., Wang D. D., Rao Y. H., Wong T. L., Raymond L. Y. K., Chen L., and Wang F. L., Incorporating user experience into critiquing-based recommender systems: A collaborative approach based on compound critiquing, Int. J. Mach. Learn.Cybern., .
[12]
Lin S. J., Integrated artificial intelligence-based resizing strategy and multiple criteria decision making technique to form a management decision in an imbalanced environment, Int. J. Mach. Learn. Cybern., .
[13]
Luo K. H., Deng Z. H., Wei L. C., and Yu H. L., JEAM: A novel model for cross-domain sentiment classification based on emotion analysis, in Proc. 2015 Conf. Empirical Methods in Natural Language Processing, Lisbon, Portugal, 2015, pp. 2503-2508.
[14]
Mohtarami M., Lan M., and Tan C. L., Probabilistic sense sentiment similarity through hidden emotions, in Proc. 51st Annual Meeting on Association for Computational Linguistics, Sofia, Bulgaria, 2013, pp. 983-992.
[15]
Hasegawa T., Kaji N., Yoshinaga N., and Toyoda M., Predicting and eliciting addressee’s emotion in online dialogue, in Proc. 51st Annual Meeting on Association for Computational Linguistics, Sofia, Bulgaria, 2013, pp. 964-972.
[16]
Qadir A. and Riloff E. M., Learning emotion indicators from tweets: Hashtags, hashtag patterns, and phrases, in Proc. 2014 Conf. Empirical Methods in Natural Language Processing, Doha, Qatar, 2014, pp. 1203-1209.
[17]
Ou G. Y., Chen W., Wang T. J., Wei Z. Y., Li B. Y., Yang D. Q., and Wong K. F., Exploiting community emotion for microblog event detection, in Proc. 2014 Conf. Empirical Methods in Natural Language Processing, Doha, Qatar, 2014, pp. 1159-1168.
[18]
Liu H. H., Li S. S., Zhou G. D., Huang C. R., and Li P. F., Joint modeling of news reader’s and comment writer’s emotions, in Proc. 51st Annual Meeting of the Association for Computational Linguistics, Sofia, Bulgaria, 2013, pp. 511-515.
[19]
Quan C. Q. and Ren F. J., Construction of a blog emotion corpus for Chinese emotional expression analysis, in Proc. 2009 Conf. Empirical Methods in Natural Language Processing, Singapore, 2009, pp. 1446-1454.
[20]
Yang M., Peng B. L., Chen Z., Zhu D. J., and Chow K. P., A topic model for building fine-grained domain-specific emotion lexicon, in Proc. 52nd Annual Meeting of the Association for Computational Linguistics (Short Papers), Baltimore, MD, USA, 2014.
[21]
Staiano J. and Guerini M., Depechemood: A lexicon for emotion analysis from crowd-annotated news, arXiv preprint arXiv: 1405.1605, 2014.
[22]
Mohammad S. M. and Turney P. D., Crowdsourcing a word-emotion association lexicon, Comput. Intell., vol. 29, no. 3, pp. 436-465, 2013.
[23]
Liu B., Sentiment analysis and opinion mining, in Synthesis Lectures on Human Language Technologies, Hirst G., ed. San Rafael: Morgan & Claypool, 2012, pp. 1-167.
[24]
Chen Y., Chai Y., Liu Y., and Xu Y., Analysis of review helpfulness based on consumer perspective, Tsinghua Sci. Technol., vol. 20, no. 3, pp. 293-305, 2015.
[25]
Tago K. and Jin Q., Influence analysis of emotional behaviors and user relationships based on twitter data, Tsinghua Sci. Technol., .
[26]
Lee S. Y. M., Chen Y., and Huang C. R., A text-driven rule-based system for emotion cause detection, in Proc. NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, Los Angeles, CA, USA, 2010, pp. 45-53.
[27]
Chen Y., Lee S. Y. M., Li S. S., and Huang C. R., Emotion cause detection with linguistic constructions, in Proc. 23rd Int. Conf. Computational Linguistics, Beijing, China, 2010, pp. 179-187.
[28]
Gui L., Yuan L., Xu R. F., Liu B., Lu Q., and Zhou Y., Emotion cause detection with linguistic construction in Chinese Weibo text, in Proc. 3rd Int. Conf. Natural Language Processing and Chinese Computing, Shenzhen, China, 2014, pp. 457-464.
[29]
Li W. Y. and Xu H., Text-based emotion classification using emotion cause extraction, Expert Syst. Appl., vol. 41, no. 4, pp. 1742-1749, 2014.
[30]
Gao K., Xu H., and Wang J. S., A rule-based approach to emotion cause detection for Chinese micro-blogs, Expert Syst. Appl., vol. 42, no. 9, pp. 4517-4528, 2015.
[31]
Ghazi D., Inkpen D., and Szpakowicz S., Detecting emotion stimuli in emotion-bearing sentences, in Proc. 2015 Int. Conf. Intelligent Text Processing and Computational Linguistics, Cairo, Egypt, 2015, pp. 152-165.
[32]
Russo I., Caselli T., Rubino F., Boldrini E., and Martínez-Barco P., Emocause: An easy-adaptable approach to emotion cause contexts, in Proc. 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis, Portland, OR, USA, 2011, pp. 153-160.
[33]
Gui L., Wu D. Y., Xu R. F., Lu Q., and Zhou Y., Event-driven emotion cause extraction with corpus construction, in Proc. 2016 Conf. Empirical Methods in Natural Language Processing, Austin, TX, USA, 2016, pp. 1639-1649.
[34]
Xu L. H., Lin H. F., Pan Y., Ren H., and Chen J. M., Constructing the affective lexicon ontology, J. China Soc. Sci. Tech. Inf., vol. 27, no. 2, pp. 180-185, 2008.
[35]
Radinsky K. and Davidovich S., Learning to predict from textual data, J. Artif. Intell. Res., vol. 45, no. 1, pp. 641-684, 2012.
[36]
Xu R. F., Zou C. T., Zheng Y. Z., Xu J., Gui L., Liu B., and Wang X. L., A new emotion dictionary based on the distinguish of emotion expression and emotion cognition, (in Chinese), J. Chin. Inf. Process., vol. 27, no. 6, pp. 82-89, 2013.
[37]
Mikolov T., Sutskever I., Chen K., Corrado G., and Dean J., Distributed representations of words and phrases and their compositionality, in Proc. 26th Int. Conf. Neural Information Processing Systems, Lake Tahoe, NV, USA, 2013, pp. 3111-3119.
