Textual emotion recognition for enhancing enterprise computing

The growing interest in affective computing (AC) brings a lot of valuable research topics that can meet different application demands in enterprise systems. The present study explores a sub area of AC techniques – textual emotion recognition for enhancing enterprise computing. Multi-label emotion recognition in text is able to provide a more comprehensive understanding of emotions than single label emotion recognition. A representation of ‘emotion state in text’ is proposed to encompass the multidimensional emotions in text. It ensures the description in a formal way of the configurations of basic emotions as well as of the relations between them. Our method allows recognition of the emotions for the words bear indirect emotions, emotion ambiguity and multiple emotions. We further investigate the effect of word order for emotional expression by comparing the performances of bag-of-words model and sequence model for multi-label sentence emotion recognition. The experiments show that the classification results under sequence model are better than under bag-of-words model. And homogeneous Markov model showed promising results of multi-label sentence emotion recognition. This emotion recognition system is able to provide a convenient way to acquire valuable emotion information and to improve enterprise competitive ability in many aspects.

[1]  Navneet Kaur,et al.  Opinion mining and sentiment analysis , 2016, 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom).

[2]  Fuji Ren,et al.  Affective Information Processing and Recognizing Human Emotion , 2006, MFCSIT.

[3]  Jen-Shin Hong,et al.  Automatic event-level textual emotion sensing using mutual action histogram between entities , 2010, Expert Syst. Appl..

[4]  Naveen Kumar,et al.  Sentence Emotion Analysis and Recognition Based on Emotion Words Using Ren-CECps ∗ , 2010 .

[5]  P. Ekman Emotion in the human face , 1982 .

[6]  Chung-Hsien Wu,et al.  Emotion recognition from text using semantic labels and separable mixture models , 2006, TALIP.

[7]  Raymond Y. K. Lau,et al.  Discovering latent commercial networks from online financial news articles , 2013, Enterp. Inf. Syst..

[8]  D. Consoli A New Concept of Marketing: The Emotional Marketing , 2010 .

[9]  Lotfi A. Zadeh,et al.  Toward Human Level Machine Intelligence - Is It Achievable? The Need for a Paradigm Shift , 2008, IEEE Computational Intelligence Magazine.

[10]  Bing Liu,et al.  Mining and summarizing customer reviews , 2004, KDD.

[11]  R. Plutchik The emotions: Facts, theories and a new model. , 1964 .

[12]  Sung-Hyon Myaeng,et al.  A Hybrid Mood Classification Approach for Blog Text , 2006, PRICAI.

[13]  Ricardo Valerdi,et al.  Guest Editorial Special Section on Enterprise Systems , 2012, IEEE Trans. Ind. Informatics.

[14]  Changqin Quan,et al.  An Exploration of Features for Recognizing Word Emotion , 2010, COLING.

[15]  Sung-Hyon Myaeng,et al.  Determining Mood for a Blog by Combining Multiple Sources of Evidence , 2007, IEEE/WIC/ACM International Conference on Web Intelligence (WI'07).

[16]  Andrew P. Martin,et al.  Using Propositional Logic for Requirements Verification of Service Workflow , 2012, IEEE Transactions on Industrial Informatics.

[17]  J. Christopher Westland,et al.  Affective data acquisition technologies in survey research , 2011, Inf. Technol. Manag..

[18]  Beihong Jin,et al.  Heuristic algorithms for effective broker deployment , 2011, Inf. Technol. Manag..

[19]  Zornitsa Kozareva,et al.  UA-ZBSA: A Headline Emotion Classification through Web Information , 2007, Fourth International Workshop on Semantic Evaluations (SemEval-2007).

[20]  Yuji Matsumoto,et al.  Emotion Classification Using Massive Examples Extracted from the Web , 2008, COLING.

[21]  Chi-Chun Lo,et al.  An evidence-based scheme for web service selection , 2011, Inf. Technol. Manag..

[22]  Josef Kittler,et al.  Pattern recognition : a statistical approach , 1982 .

[23]  Antonio Camurri,et al.  Toward a Minimal Representation of Affective Gestures , 2011, IEEE Transactions on Affective Computing.

[24]  Fuji Ren Robotics cloud and robotics school , 2011, 2011 7th International Conference on Natural Language Processing and Knowledge Engineering.

[25]  Fuji Ren,et al.  From Cloud Computing to Language Engineering, Affective Computing and Advanced Intelligence ∗ , 2010 .

[26]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[27]  Pat Langley,et al.  Estimating Continuous Distributions in Bayesian Classifiers , 1995, UAI.

[28]  Jonathon Read,et al.  Recognising Affect in Text using Pointwise-Mutual Information , 2004 .

[29]  Wan Ishak Wan Ismail,et al.  Outdoor colour recognition system for oil palm fresh fruit bunches (ffb) , 2010 .

[30]  J. R. Quinlan Constructing Decision Trees , 1993 .

[31]  Maja Pantic,et al.  Dynamics of facial expression: recognition of facial actions and their temporal segments from face profile image sequences , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[32]  Lida Xu,et al.  Modelling and analysis of workflow for lean supply chains , 2011, Enterp. Inf. Syst..

[33]  Joseph Kaye,et al.  Understanding how bloggers feel: recognizing affect in blog posts , 2006, CHI Extended Abstracts.

[34]  Alberto Maria Segre,et al.  Programs for Machine Learning , 1994 .

[35]  Juhnyoung Lee,et al.  Variable pricing business solutions in a decomposed business environment , 2007, Enterp. Inf. Syst..

[36]  Lida Xu,et al.  AutoAssem: An Automated Assembly Planning System for Complex Products , 2012, IEEE Transactions on Industrial Informatics.

[37]  Andrew P. Martin,et al.  SWSpec: The Requirements Specification Language in Service Workflow Environments , 2012, IEEE Transactions on Industrial Informatics.

[38]  Hooshang M. Beheshti,et al.  Improving productivity and firm performance with enterprise resource planning , 2010, Enterp. Inf. Syst..

[39]  François Yvon,et al.  The Contribution of Low Frequencies to Multilingual Sub-sentential Alignment: a Differential Associative Approach , 2011 .

[40]  CarlettaJean Assessing agreement on classification tasks , 1996 .

[41]  Hara Kostakis,et al.  A new method for activity-based modelling of customer profitability analysis in hotels , 2011, Int. J. Adv. Intell. Paradigms.

[42]  Henry Lieberman,et al.  A model of textual affect sensing using real-world knowledge , 2003, IUI '03.

[43]  Ron Kohavi,et al.  The Power of Decision Tables , 1995, ECML.

[44]  Bo Pang,et al.  Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.

[45]  Andrea Kleinsmith,et al.  Affective Body Expression Perception and Recognition: A Survey , 2013, IEEE Transactions on Affective Computing.

[46]  Cecilia Ovesdotter Alm,et al.  Emotions from Text: Machine Learning for Text-based Emotion Prediction , 2005, HLT.

[47]  William M. Pottenger,et al.  Classification of Emotions in Internet Chat: An Application of Machine Learning Using Speech Phonemes , 2003 .

[48]  Hugo Liu,et al.  A Corpus-based Approach to Finding Happiness , 2006, AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs.

[49]  Jingbo Zhu,et al.  Aspect-Based Opinion Polling from Customer Reviews , 2011, IEEE Transactions on Affective Computing.

[50]  Rafael A. Calvo,et al.  Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications , 2010, IEEE Transactions on Affective Computing.

[51]  Xiaozhu Chen,et al.  Enterprise systems in financial sector – an application in precious metal trading forecasting , 2013, Enterp. Inf. Syst..

[52]  Michael Wagner,et al.  Evaluating AAM fitting methods for facial expression recognition , 2009, 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops.

[53]  E. Vesterinen,et al.  Affective Computing , 2009, Encyclopedia of Biometrics.

[54]  M. Markus,et al.  The Enterprise System Experience— From Adoption to Success , 2000 .

[55]  Rosalind W. Picard Affective computing: (526112012-054) , 1997 .

[56]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[57]  Mitsuru Ishizuka,et al.  SentiFul: A Lexicon for Sentiment Analysis , 2011, IEEE Transactions on Affective Computing.

[58]  Josef Basl,et al.  Introduction: advances in E-business engineering , 2012, Inf. Technol. Manag..

[59]  Oren Etzioni,et al.  Extracting Product Features and Opinions from Reviews , 2005, HLT.

[60]  Carlo Strapparava,et al.  SemEval-2007 Task 14: Affective Text , 2007, Fourth International Workshop on Semantic Evaluations (SemEval-2007).

[61]  Fuji Ren,et al.  Analysis of Wakamono Kotoba Emotion Corpus and Its Application in Emotion Estimation , 2011 .

[62]  D. Kibler,et al.  Instance-based learning algorithms , 2004, Machine Learning.

[63]  Changqin Quan,et al.  Emotion analysis in blogs at sentence level using a Chinese emotion corpus , 2010, Proceedings of the 6th International Conference on Natural Language Processing and Knowledge Engineering(NLPKE-2010).

[64]  Andrew Ortony,et al.  The Referential Structure of the Affective Lexicon , 1987, Cogn. Sci..

[65]  Gilad Mishne,et al.  Capturing Global Mood Levels using Blog Posts , 2006, AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs.

[66]  Shrikanth S. Narayanan,et al.  Toward detecting emotions in spoken dialogs , 2005, IEEE Transactions on Speech and Audio Processing.

[67]  Lihong Liang,et al.  Earnings forecasts in enterprise information systems environment , 2008, Enterp. Inf. Syst..

[68]  Wu He,et al.  Distributed data mining for e-business , 2011, Inf. Technol. Manag..

[69]  Jean Carletta,et al.  Assessing Agreement on Classification Tasks: The Kappa Statistic , 1996, CL.

[70]  Maria Leonor Pacheco,et al.  of the Association for Computational Linguistics: , 2001 .

[71]  Peter D. Turney Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews , 2002, ACL.

[72]  Rohini K. Srihari,et al.  OpinionMiner: a novel machine learning system for web opinion mining and extraction , 2009, KDD.

[73]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[74]  G. A. Mishne,et al.  Expiriments with mood classification in blog posts , 2005, SIGIR 2005.

[75]  Hsin-Hsi Chen,et al.  What emotions do news articles trigger in their readers? , 2007, SIGIR.

[76]  Changqin Quan,et al.  A blog emotion corpus for emotional expression analysis in Chinese , 2010, Comput. Speech Lang..

[77]  Sunil Erevelles,et al.  Consumer Satisfaction for Internet Service Providers: An Analysis of Underlying Processes , 2003, Inf. Technol. Manag..

[78]  Richard L. Smith,et al.  PREDICTIVE INFERENCE , 2004 .

[79]  Gomase Vs,et al.  Immunoproteomics approach for fragment based vaccine design from Coxsackie B virus , 2009 .