Skip to main content
SpringerLink
Log in

A review of emotion sensing: categorization models and algorithms

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Sentiment analysis consists in the identification of the sentiment polarity associated with a target object, such as a book, a movie or a phone. Sentiments reflect feelings and attitudes, while emotions provide a finer characterization of the sentiments involved. With the huge number of comments generated daily on the Internet, besides sentiment analysis, emotion identification has drawn keen interest from different researchers, businessmen and politicians for polling public opinions and attitudes. This paper reviews and discusses existing emotion categorization models for emotion analysis and proposes methods that enhance existing emotion research. We carried out emotion analysis by inviting experts from different research areas to produce comprehensive results. Moreover, a computational emotion sensing model is proposed, and future improvements are discussed in this paper.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Cambria E, Poria S, Gelbukh A, Thelwall M (2017) Sentiment analysis is a big suitcase. IEEE Intell Syst 32(6):74–80

    Article  Google Scholar 

  2. Wang Z, Tong JC, Chan D (2014) Issues of social data analytics with a new method for sentiment analysis of social media data. In: IEEE international conference on cloud computing technology and science, pp 899–904

    Google Scholar 

  3. Wang Z, Chong CS, Lan L, Yang Y, Ho S, Tong JC (2016) Fine-grained sentiment analysis of social media with emotion sensing. In: Future Technologies Conference (FTC), pp 1361–1364

    Chapter  Google Scholar 

  4. Balahur A, Hermida JM, Montoyo A, Muñoz R (2013) Detecting implicit expressions of affect in text using EmotiNet and its extensions. Data Knowl Eng 88:113–125

    Article  Google Scholar 

  5. Shaver P, Schwartz J, Kirson D, O’Connor C (1987) Emotion knowledge: further exploration of a prototype approach. J Pers Soc Psychol 52(6):1061–1086

    Article  Google Scholar 

  6. Parrott WG (2001) Emotions in social psychology: essential readings. Psychology Press, Philadelphia

    Google Scholar 

  7. Ortony A, Turner TJ (1990) What’ s basic about basic emotions? Psychol Rev 97(3):315–331

    Article  Google Scholar 

  8. Ortony A, Clore G, Collins A (1988) The cognitive structure of emotions. Cambridge University Press, Cambridge

    Book  Google Scholar 

  9. Steunebrink BR, Dastani M, Meyer JC (2009) The OCC model revisited. In: Proceedings of 4th Work. Emot. Comput

    Google Scholar 

  10. Ekman P (1992) An argument for basic emotions. Cognit Emot 6:169–200

    Article  Google Scholar 

  11. Ghazi D, Inkpen D, Szpakowicz S (2014) Prior and contextual emotion of words in sentential context. Comput Speech Lang 28(1):76–92

    Article  Google Scholar 

  12. Chafale D, Pimpalkar A (2014) Review on developing corpora for sentiment analysis using plutchik. Int J Comput Sci Eng 2(10)

  13. Plutchik R (2001) The nature of emotions human emotions have deep evolutionary roots, a fact that may explain their complexity and provide tools for clinical practice. Am Sci 89(4):344–350

    Article  Google Scholar 

  14. Neviarouskaya A, Prendinger H, Ishizuka M (2007) Textual affect sensing for sociable and expressive online communication. Affect Comput Intell Interact:218–229

  15. Suttles J, Ide N (2013) Distant supervision for emotion classification with discrete binary values. LNCS 7817:121–136

    Google Scholar 

  16. Yu Y, Wang X (2015) World cup 2014 in the Twitter world: a big data analysis of sentiments in U.S. sports fans’ tweets. Comput Hum Behav 48:392–400

    Article  Google Scholar 

  17. Mehrabian A (1996) Pleasure-arousal-dominance: a general framework for describing and measuring individual differences in temperament. Curr Psychol 14(4):261–292

    Article  MathSciNet  Google Scholar 

  18. Ashkanasy NM, Cooper CL (2008) Research companion to emotion in organizations. Edward Elgar Publishing, Cheltenham

    Book  Google Scholar 

  19. Latinjak AT (2012) The underlying structure of emotions: a tri-dimensional model of core affect and emotion concepts for sports. Rev Iberoam Psicol Del Ejerc Yel Deport 7(1):71–87

    Google Scholar 

  20. Cambria E, Livingstone A, Hussain A (2012) The hourglass of emotions. Cogn Behav Syst:144–157

  21. Robinson DL (2008) Brain function, mental experience and personality. Neth J Psychol 64(4):152–167

    Google Scholar 

  22. Schulz A, Paulheim H, Schweizer I (2013) A fine-grained sentiment analysis approach for detecting crisis related microposts. In: Proceedings of the 10th international ISCRAM conference, pp 846–851

    Google Scholar 

  23. Reeck C, Ames DR, Ochsner KN (2016) The social regulation of emotion: an integrative, cross-disciplinary model. Trends Cogn Sci 20(1):47–63

    Article  Google Scholar 

  24. Lerner J, Li Y, Valdesolo P, Kassam K (2015) Emotion and decision making: online supplement. Annu Rev Psychol 66:1–14

    Article  Google Scholar 

  25. Li H, Pang N, Guo S, Wang H (2007) Research on textual emotion recognition incorporating personality factor. In: Robotics and biomimetics, pp 2222–2227

    Google Scholar 

  26. Huangfu L, Mao W, Zeng D, Wang L (2013) OCC model-based emotion extraction from online reviews. In: IEEE Int. Conf. Intell. Secur. Informatics Big Data, Emergent Threat. Decis. Secur. Informatics, pp 116–121

    Google Scholar 

  27. Soares AP, Comesaña M, Pinheiro AP, Simões A, Frade CS (2012) The adaptation of the affective norms for English words (ANEW) for European Portuguese. Behav Res Methods 44:256–269

    Article  Google Scholar 

  28. Shivhare SN (2015) EmotionFinder: detecting emotion from blogs and textual documents. In: Comput. Commun. Autom. (ICCCA), pp 52–57

    Google Scholar 

  29. Cambria E, Hussain A, Havasi C, Eckl C (2010) Sentic computing: exploitation of common sense for the development of emotion-sensitive systems. In: Development of multimodal interfaces: active listening and synchrony. Springer, Berlin/Heidelberg, pp 148–156

    Chapter  Google Scholar 

  30. Cambria E, Poria S, Hazarika D, Kwok K (2018) SenticNet 5: discovering conceptual primitives for sentiment analysis by means of context embeddings. In: Proceedings of AAAI, pp 1795–1802

    Google Scholar 

  31. Poria S, Cambria E, Winterstein G, Huang G-B (2014) Sentic patterns: dependency-based rules for concept-level sentiment analysis. Knowl Based Syst 69:45–63

    Article  Google Scholar 

  32. Ma Y, Peng H, Cambria E (2018) Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of AAAI, pp 5876–5883

    Google Scholar 

  33. Poria S, Chaturvedi I, Cambria E, Hussain A (2016) Convolutional MKL based multimodal emotion recognition and sentiment analysis. In: IEEE international conference on data mining (ICDM), pp 439–448

    Google Scholar 

  34. Wang Z, Tong JC, Ruan P, Li F (2016) Lexicon knowledge extraction with sentiment polarity computation. In: IEEE international conference on data mining (ICDM), SENTIRE, pp 978–983

    Google Scholar 

  35. Wang Z, Tong JC, Xin X, Chin HC (2014) Anomaly detection through enhanced sentiment analysis on social media data. In: IEEE international conference on cloud computing technology and science, pp 917–922

    Google Scholar 

  36. L. Gui, D. Wu, R. Xu, Q. Lu, and Y. Zhou, “Event-driven emotion cause extraction with corpus construction,” in Proceedings of empirical methods in natural language processing, pp. 1639–1649, 2016.

  37. Ding Z, He H, Zhang M, Xia R (2019) From independent prediction to reordered prediction: integrating relative position and global label information to emotion cause identification. In: Proc. AAAI

    Google Scholar 

  38. Xia R, Zhang M, Ding Z (2019) RTHN: a RNN-transformer hierarchical network for emotion cause extraction. In: Proceedings of international joint conferences on artificial intelligence (IJCAI)

    Google Scholar 

  39. Xia R, Ding Z (2019) Emotion-cause pair extraction: a new task to emotion analysis in texts. In: Proceedings of the association for computational linguistics (ACL)

    Google Scholar 

  40. Wang Z, Goh R, Yang Y (2014) A method and system for sentiment classification and emotion classification. SG Patent Application No.: 11201704150W, US Patent Application No.: 15/523,201

  41. Wang Z, Tong JC (2015) ChiEFS-A method and system for Chinese hybrid multilingual emotion fine-grained sensing of text data. Singapore Patent Application No 10201601413Q

  42. Chikersal P, Poria S, Cambria E, Gelbukh A, Siong CE (2015) Modelling public sentiment in Twitter: using linguistic patterns to enhance supervised learning. Comput Linguist Intell Text Process:49–65

  43. Cambria E, Poria S, Bisio F, Bajpai R, Chaturvedi I (2015) The CLSA model: a novel framework for concept-level sentiment analysis. Comput Linguist Intell Text Process 9042:3–22

    Google Scholar 

  44. Howard N, Cambria E (2013) Intention awareness: improving upon situation awareness in human-centric environments. Hum Centric Comput Inf Sci 3(9)

  45. Cambria E, Benson T, Eckl C, Hussain A (2012) Sentic PROMs: application of Sentic computing to the development of a novel unified framework for measuring health-care quality. Expert Syst Appl 39(12):10533–10543

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information Systems, Singapore Management University, 80 Stamford Rd, Singapore, 178902, Singapore

    Zhaoxia Wang

  2. Institute of High Performance Computing, A*STAR, 1 Fusionopolis Way, Singapore, 138632, Singapore

    Seng-Beng Ho

  3. School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore

    Erik Cambria

Authors
  1. Zhaoxia Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Seng-Beng Ho
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Erik Cambria
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Erik Cambria.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendices

Appendix 1

Table 7 A tree-structured list of emotions described in Shaver model and also featured by Parrott [5, 6]
Full size table

Appendix 2

Fig. 3
figure 3

The structure of emotions of the OCC Model [8]

Full size image

Appendix 3

Fig. 4
figure 4

A disambiguated, inheritance-based hierarchy of emotions of the OCC Model (Revised OCC Model)

Full size image

Appendix 4

Fig. 5
figure 5

Plutchik’s Wheel of Emotions [12, 13]

Full size image

Appendix 5

Table 8 Complex Emotions are a composition of basic emotions [12, 13]
Full size table

Appendix 6

Table 9 Second-level emotions derived from the combinations of different sentic levels of each of the affective dimensions [20]
Full size table

Appendix 7

Table 10 Eleven positive emotions and their causes and consequences [21]
Full size table

Appendix 8

Table 11 Eleven negative emotions and their causes and consequences [21]
Full size table

Appendix 9

Table 12 Emotion type specifications of the 65 emotions which are extended from reviewed literatures by this research
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Z., Ho, SB. & Cambria, E. A review of emotion sensing: categorization models and algorithms. Multimed Tools Appl 79, 35553–35582 (2020). https://doi.org/10.1007/s11042-019-08328-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-08328-z

Keywords

Search

Navigation