Textual emotion recognition for enhancing enterprise computing

References

• Aha, D., D. Kibler, and M. K. Albert. 1991. “Instance-Based Learning Algorithms.” Machine Learning 6: 37–66. doi:10.1007/BF00153759.
   Web of Science ®Google Scholar
• Alm, C. O., D. Roth, and R. Sproat. 2005. “Emotions from Text: Machine Learning for Text-Based Emotion Prediction.” In Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, Vancouver, BC, 579–586. Stroudsburg, PA: The Association for Computational Linguistics.
   Google Scholar
• Asthana, A., J. Saragih, M. Wagner, and R. Goecke. 2009. “Evaluating AAM Fitting Methods for Facial Expression Recognition.” Proceedings of 2009 International Conference on Affective Computing and Intelligent Interaction. Amsterdam, September 10–12.
   Google Scholar
• Averill, J. R. 1975. “A Semantic Atlas of Emotional Concepts.” JSAS Catalog of Selected Documents in Psychology 5: 530 (Ms. No. 421).
   Google Scholar
• Beheshti, H., and C. Beheshti. 2010. “Improving Productivity and Firm Performance with Enterprise Resource Planning.” Enterprise Information Systems 4 (4): 445–472. doi:10.1080/17517575.2010.511276.
   Web of Science ®Google Scholar
• Calvo, R., and S. D’Mello. 2010. “Affect Detection: An Interdisciplinary Review of Models, Methods, and their Applications.” IEEE Transactions on Affective Computing 1 (1): 18–37. doi:10.1109/T-AFFC.2010.1.
   Web of Science ®Google Scholar
• Carletta, J. 1996. “Assessing Agreement on Classification Tasks: The Kappa Statistic.” Computational Linguistics 22 (2): 249–254.
   Web of Science ®Google Scholar
• Chen, X., and Y. Fang. 2012. “Enterprise Systems in Financial Sector – An Application in Precious Metal Trading Forecasting.” Enterprise Information Systems 7 (4): 558–568.
   Web of Science ®Google Scholar
• Consoli, D. 2012. “A New Concept of Marketing: The Emotional Marketing.” Broad Research in Accounting, Negotiation, and Distribution 1 (1): 52–59.
   Google Scholar
• Devijver, P. A., and J. Kittler. 1982. Pattern Recognition: A Statistical Approach. London: Prentice-Hall.
   Google Scholar
• Ekman, P. 1982. Emotion in the Human Face. New York, NY: Cambridge University Press.
   Google Scholar
• Erevelles, S., S. Srinivasan, and S. Rangel. 2003. “Consumer Satisfaction for Internet Service Providers: An Analysis of Underlying Processes.” Information Technology and Management 4 (1): 69–89. doi:10.1023/A:1021828517151.
   Google Scholar
• Flaxer, D., R. Z. Cao, C. Tian, W. Ding, and J. Lee. 2007. “Variable Pricing Business Solutions in a Decomposed Business Environment.” Enterprise Information Systems 1 (2): 177–195. doi:10.1080/17517570701254965.
   Web of Science ®Google Scholar
• Freund, Y., and R. Schapire. 1996. “Experiments with a New Boosting Algorithm.” In Proceedings of Thirteenth International Conference on Machine Learning, 148–156. San Francisco, CA: Morgan Kaufmann Publishers.
   Google Scholar
• Geisser, S. 1993. Predictive Inference. New York, NY: Chapman and Hall.
   Google Scholar
• Glowinski, D., N. Dael, A. Camurri, G. Volpe, M. Mortillaro, and K. Scherer. 2011. “Toward a Minimal Representation of Affective Gestures.” IEEE Transactions on Affective Computing 2 (2): 106–118. doi:10.1109/T-AFFC.2011.7.
   Web of Science ®Google Scholar
• Holzman, L., and W. Pottenger. 2003. Classification of Emotions in Internet Chat: An Application of Machine Learning Using Speech Phonemes. Technical Report LU-CSE-03-002. Bethlehem, PA: Lehigh University.
   Google Scholar
• Hu, M., and B. Liu. 2004. “Mining and Summarizing Customer Reviews.” In Proceedings of Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 168–177. New York: ACM Digital Library Publisher.
   Google Scholar
• Jin, W., H. Ho, and R. Srihari. 2009. “OpinionMiner: A Novel Machine Learning System for Web Opinion Mining and Extraction.” In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1195–1204. Paris: ACM Digital Library Publisher.
   Google Scholar
• John, G., and P. Langley. 1995. “Estimating Continuous Distributions in Bayesian Classifiers.” In Proceedings of Eleventh Conference on Uncertainty in Artificial Intelligence, Quebec, 338–345. San Francisco, CA: Morgan Kaufmann Publishers.
   Google Scholar
• Jung, Y., Y. Choi, and S. H. Myaeng. 2007. “Determining Mood for a Blog by Combining Multiple Sources of Evidence.” In Proceedings of IEEE/WIC/ACM International Conference on Web Intelligence, 271–274. Washington, DC: IEEE Computer Society.
   Google Scholar
• Jung, Y., H. Park, and S. H. Myaeng. 2006. “A Hybrid Mood Classification Approach for Blog Text.” Lecture Notes in Computer Science 4099: 1099–1103. doi:10.1007/978-3-540-36668-3_141.
   Google Scholar
• Kleinsmith, A., and N. Bianchi-Berthouze. 2012. “Affective Body Expression Perception and Recognition: A Survey.” IEEE Transactions on Affective Computing 4 (1): 15–33.
   Web of Science ®Google Scholar
• Kohavi, R. 1995a. “A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection.” Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, Vol. 2, 1137–1143. San Francisco, CA: Morgan Kaufmann Publishers.
   Google Scholar
• Kohavi, R. 1995b. “The Power of Decision Tables.” In Proceedings of 8th European Conference on Machine Learning, 174–189. London: Springer-Verlag.
   Google Scholar
• Kozareva, Z., B. Navarro, S. Vazquez, and A. Montoyo. 2007. “UA-ZBSA: A Headline Emotion Classification through Web Information.” In Proceedings of the 4th International Workshop on the Semantic Evaluations (SemEval 2007), Prague, Czech Republic, 334–337. Stroudsburg, PA: Association for Computational Linguistics.
   Google Scholar
• Lardilleux, A., Y. Lepage, and F. Yvon. 2011. “The Contribution of Low Frequencies to Multilingual Sub-sentential Alignment: A Differential Associative Approach.” International Journal of Advanced Intelligence 3 (2): 189–217.
   Google Scholar
• Lee, C. M., and S. S. Narayanan. 2005. “Toward Detecting Emotions in Spoken Dialogs.” IEEE Transactions on Speech and Audio Processing 13 (2): 293–303.
   Google Scholar
• Leshed, G., and J. Kaye. 2006. “Understanding How Bloggers Feel: Recognizing Affect in Blog Posts.” In Proceedings of SIGCHI Conference on Human Factors in Computing Systems, 1019–1024. New York: ACM Press.
   Google Scholar
• Li, L. 2011. “Introduction: Advances in E-Business Engineering.” Information Technology and Management 12 (2): 49–50. doi:10.1007/s10799-011-0100-y.
   Web of Science ®Google Scholar
• Liang, L. 2008. “Earnings Forecasts in Enterprise Information Systems Environment.” Enterprise Information Systems 2 (1): 1–19. doi:10.1080/17517570701846539.
   Web of Science ®Google Scholar
• Lin, K. H. Y., C. Yang, and H. H. Chen. 2007. “What Emotions Do News Articles Trigger in Their Readers?” In Proceedings of the 30th Annual International ACM SIGIR Conference, Amsterdam, 733–734. New York: ACM Press.
   Google Scholar
• Liu, B., S. Cao, and W. He. 2011. “Distributed Data Mining for E-Business.” Information Technology and Management 12 (2): 67–79. doi:10.1007/s10799-011-0091-8.
   Web of Science ®Google Scholar
• Liu, H., H. Lieberman, and T. Selker. 2003. “A Model of Textual Affect Sensing Using Real-World Knowledge.” In Proceedings of the 2003 International Conference on Intelligent User Interfaces, 125–132. New York: ACM Press.
   Google Scholar
• Lu, C. Y., S. H. Lin, J. C. Liu, S. Cruz-Lara, and J. S. Hong. 2010. “Automatic Event-Level Textual Emotion Sensing Using Mutual Action Histogram between Entities.” Expert Systems with Applications 37 (2): 1643–1653. doi:10.1016/j.eswa.2009.06.099.
   Web of Science ®Google Scholar
• Maab, J., K. Wang, and L. Xu. 2011. “Modelling and Analysis of Workflow for Lean Supply Chains.” Enterprise Information Systems 5 (4): 423–447. doi:10.1080/17517575.2011.580007.
   Web of Science ®Google Scholar
• Mahmoud, N., B. Mohamed, and G. Gouard è Res. 2007. “Affective Virtual Learning Environments.” In Frontiers in Enterprise Integration Xian International Symposium, IFISF and IFIP TC 8.9 International Conference on Research and Practical Issues of Enterprise Information Systems, CONFENIS, 301–312. Beijing: Springer.
   Google Scholar
• Markus, M., and C. Tanis. 2000. “The Enterprise System Experience – From Adoption to Success.” In Framing the Domains of IT-Management: Projecting the Future Through the PastPinnaflex Educational Resources, edited by R. W. Zmud, 173–207. Cincinnati, OH: Pinnaflex Educational Resources Inc.
   Google Scholar
• Matsumoto, K., and Y. Konishi. 2011. “Hidemichi Sayama and Fuji Ren, Analysis of Wakamono Kotoba Emotion Corpus and Its Application in Emotion Estimation.” International Journal of Advanced Intelligence 3 (1): 1–24.
   Google Scholar
• Mihalcea, R., and H. Liu. 2006. “A Corpus-Based Approach to Finding Happiness.” In AAAI Spring Symposium on Computational Approaches to Analysing Weblogs, Menlo Park, CA, 139–144. Baltimore, MD: University of Maryland.
   Google Scholar
• Mishne, G. 2005. “Experiments with Mood Classification in Blog Posts.” In Proceedings of the Style2005: The 1st Workshop on Stylistic Analysis of Text for Information Access, SIGIR 2005. New York: ACM Press.
   Google Scholar
• Mishne, G. 2006. “Capturing Global Mood Levels Using Blog Posts.” In Proceedings of AAAI 2006 Spring Symposium on Computational Approaches to Analysing Weblogs, 145–152. Baltimore, MD: University of Maryland.
   Google Scholar
• Neviarouskaya, A., H. Prendinger, and M. Ishizuka. 2011. “SentiFul: A Lexicon for Sentiment Analysis.” IEEE Transactions on Affective Computing 2 (1): 22–36. doi:10.1109/T-AFFC.2011.1.
   Web of Science ®Google Scholar
• Ortony, A., G. L. Clore, and M. A. Foss. 1987. “The Referential Structure of the Affective Lexicon.” Cognitive Science 11: 341–364. doi:10.1207/s15516709cog1103_4.
   Web of Science ®Google Scholar
• Pang, B., and L. Lee. 2008. “Opinion Mining and Sentiment Analysis.” Foundations and Trends in Information Retrieval 2: 1–135. doi:10.1561/1500000011.
   Google Scholar
• Pang, B., L. Lee, and S. Vaithyanathan. 2002. “Thumbs Up?: Sentiment Classification Using Machine Learning Techniques.” Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing – EMNLP ’02 10: 79–86. doi:10.3115/1118693.1118704.
   Google Scholar
• Pantic, M., and I. Patras. 2006. “Dynamics of Facial Expression: Recognition of Facial Actions and Their Temporal Segments from Face Profile Image Sequences.” IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 36 (2): 433–449. doi:10.1109/TSMCB.2005.859075.
   PubMed Web of Science ®Google Scholar
• Picard, R. W. 1997. Affective Computing. Cambridge, MA: The MIT Press.
   Google Scholar
• Plutchik, R. 1962. The Emotions: Facts, Theories and a New Model. New York, NY: Random House.
   Google Scholar
• Popescu, A., and O. Etzioni. 2005. “Extracting Product Features and Opinions from Reviews.” In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, 339–346. Stroudsburg, PA: Association for Computational Linguistics.
   Google Scholar
• Qian, Y., B. Jin, and W. Fang. 2011. “Heuristic Algorithms for Effective Broker Deployment.” Information Technology and Management 12 (2): 55–66. doi:10.1007/s10799-011-0095-4.
   Web of Science ®Google Scholar
• Quan, C., and F. Ren. 2010a. “A Blog Emotion Corpus for Emotional Expression Analysis in Chinese.” Computer Speech and Language 24 (4): 726–749. doi:10.1016/j.csl.2010.02.002.
   Web of Science ®Google Scholar
• Quan, C., and F. Ren. 2010b. “An Exploration of Features for Recognizing Word Emotion.” In Proceedings of the 23rd International Conference on Computational Linguistics (COLING2010), 922–930. Beijing: Chinese Information Processing Society of China.
   Google Scholar
• Quan, C., and F. Ren. 2010c. “Sentence Emotion Analysis and Recognition Based on Emotion Words Using Ren-CECps.” International Journal of Advanced Intelligence 2 (1), 105–107.
   Google Scholar
• Quan, C., F. Ren, and T. He. 2010. “Emotion Analysis in Blogs at Sentence Level Using a Chinese Emotion Corpus.” Proceedings of 2010 IEEE International Conference on Natural Language Processing and Knowledge Engineering (IEEE NLP-KE 2010), Beijing, August 21–23, 478–484.
   Google Scholar
• Quinlan, R. 1993. C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann.
   Google Scholar
• Read, J. 2004. “Recognising Affect in Text Using Pointwise-Mutual Information.” Thesis (Master), University of Sussex.
   Google Scholar
• Ren, F. 2009. “Affective Information Processing and Recognizing Human Emotion.” Electronic Notes in Theoretical Computer Science 225: 39–50. doi:10.1016/j.entcs.2008.12.065.
   Google Scholar
• Ren, F. 2010. “From Cloud Computing to Language Engineering, Affective Computing and Advanced Intelligence.” International Journal of Advanced Intelligence 2 (1): 1–14.
   Google Scholar
• Ren, F. 2011. “Robotics Cloud and Robotics School.” In Proceedings of 2011 IEEE International Conference on Natural Language Processing and Knowledge Engineering (IEEE NLP-KE 2011), 1–8. Tokushima: IEEE.
   Google Scholar
• Strapparava, C. 2007. “Semeval-2007 Task 14: Affective Text.” Proceedings of the 4th International Workshop on the Semantic Evaluations (SemEval 2007), Prague, Czech Republic.
   Google Scholar
• Tokuhisa, R., K. Inui, and Y. Matsumoto. 2008. “Emotion Classification Using Massive Examples Extracted from the Web.” In Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), 881–888. Manchester: Coling 2008 Organizing Committee.
   Google Scholar
• Turney, P. 2002. “Thumbs up or Thumbs Down?: Semantic Orientation Applied to Unsupervised Classification of Reviews.” In Proceedings of the 40th annual meeting on association for computational linguistics ACL’02, 417–424. Stroudsburg, PA: Association for Computational Linguistics.
   Google Scholar
• Viriyasitavat, W., L. Xu, and A. Martins. 2012. “SWSpec: The Requirements Specification Language in Service Workflow Environments.” IEEE Transactions on Industrial Informatics 8 (3): 631–638. doi:10.1109/TII.2011.2182519.
   Web of Science ®Google Scholar
• Wang, P., K. Chao, C. Lo, and R. Farmer. 2011. “An Evidence-Based Scheme for Web Service Selection.” Information Technology and Management 12 (2): 161–172. doi:10.1007/s10799-011-0098-1.
   Web of Science ®Google Scholar
• Westland, J. C.. 2011. “Affective Data Acquisition Technologies in Survey Research.” Information Technology and Management 12 (4): 387–408. doi:10.1007/s10799-011-0110-9.
   Web of Science ®Google Scholar
• Wu, C., Z. Chuang, and Y. Lin. 2006. “Emotion Recognition from Text Using Semantic Labels and Separable Mixture Models.” ACM Transactions on Asian Language Information Processing 5 (2): 165–183. doi:10.1145/1165255.1165259.
   Google Scholar
• Xia, Y., W. Su, R. Lau, and Y. Liu. 2011. “Discovering Latent Commercial Networks from Online Financial News Articles.” Enterprise Information Systems 7 (3): 303–331.
   Web of Science ®Google Scholar
• Xu, L., W. Viriyasitavat, P. Ruchikachorn, and A. Martins. 2012a. “Using Propositional Logic for Requirements Verification of Service Workflow.” IEEE Transactions on Industrial Informatics 8 (3): 639–646. doi:10.1109/TII.2012.2187908.
   Web of Science ®Google Scholar
• Xu, L., C. Wang, Z. Bi, and J. Yu. 2012b. “Autoassem: An Automated Assembly Planning System for Complex Products.” IEEE Transactions on Industrial Informatics 8 (3): 669–678. doi:10.1109/TII.2012.2188901.
   Web of Science ®Google Scholar
• Zadeh, L. 2009. “Toward Human Level Machine Intelligence – Is It Achievable? The Need for a Paradigm Shift.” International Journal of Advanced Intelligence 1 (1): 1–26.
   Google Scholar
• Zhou, Z., R. Valerdi, and S. Zhou. 2012. “Guest Editorial Special Section on Enterprise Systems.” IEEE Transactions on Industrial Informatics 8 (3): 630. doi:10.1109/TII.2012.2197004.
   Web of Science ®Google Scholar
• Zhu, J., H. Wang, M. Zhu, B. Tsou, and M. Ma. 2011. “Aspect-Based Opinion Polling from Customer Reviews.” IEEE Transactions on Affective Computing 2 (1): 37–49. doi:10.1109/T-AFFC.2011.2.
   Web of Science ®Google Scholar