Skip to main content
SpringerLink
Log in

Relationship Identification Between Conversational Agents Using Emotion Analysis

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

Human relationships are influenced by the underlying emotions in their interactions. With the increasing use of social networks, relationships from textual data can also be inferred from online interactions. Such interactions result in massive amount of textual data which is available in the form of text messages, emails, and social media posts. Identification and analysis of human relationships are useful for numerous applications ranging from cybersecurity to public health. In this paper, we present a method called RIEA (Relationship Identification using Emotion Analysis), for identifying relationships between multiple intelligent agents by analyzing the conversation between them. The objective of our work is to combine concepts of cognitive psychology and natural language processing (NLP) to extract emotions and map them onto a set of relationships and analyze how relationships transform over time. We employ psychological models to label a large corpus of conversations and apply machine learning techniques to determine emotion-to-relationship mapping. We use four distinct association classes and four attachment styles using best-worst scaling method for classification. Combining the attachment and association styles given in research literature gives us the relationship combinations for our analysis. Additionally, this work studies the most common changes of behaviors and emotions and the corresponding transformations in human relationships. Our results show that RIEA can correctly detect interpersonal relationships with an accuracy of 85%. The evaluation shows that RIEA can accurately identify interpersonal relationships from conversations and can be extended for identifying more complex relationships. This study also highlights the effect of changes in emotional behavior in the development of relationships over time.

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
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Notes

  1. https://github.com/Sairaqamar591/RIEA

References

  1. Al-Ghadir AI, Azmi AM. A study of arabic social media users—posting behavior and author’s gender prediction. Cognitive Computation 2019;11:71–86.

    Article  Google Scholar 

  2. Liu C-C, Yang T-H, Hsieh C-T, Soo V-W. Towards text-based emotion detection. A survey and possible improvements. International conference on information management and engineering; 2009. p. 70–74.

  3. Flynn TN, Marley AAJ. 2014. Best-worst scaling:theory and methods. Stephane Hess and Andrew Daly. Handbook of Choice Modelling. Edward Elgar Publishing.

  4. Fox J, Tokunaga RS. Romantic partner monitoring after breakups: attachment, dependence, distress, and post-dissolution online surveillance via social networking sites. Cyberpsychology, Behavior, and Social Networking 2015;18(9):491–498.

    Article  Google Scholar 

  5. Plutchik R. 2003. Emotions and life: perspectives from psychology, biology, and evolution. American Psychological Association.

  6. Bravo-Marquez F, Frank E, Mohammad S, Pfahringer B. Determining word-emotion associations from tweets by multi-label classification. IEEE/WIC/ACM international conference on web intelligence (WI); 2016. p. 536–539.

  7. Bourgais M, Taillandier P, Vercouter L. 2017. Enhancing the behavior of agents in social simulations with emotions and social relations. Multi-Agent Based Simulation: 89–104.

  8. Singh D, Padgham L, Logan B. Integrating BDI agents with agent-based simulation platforms. Auton Agent Multi-Agent Syst 2016;30(6):1050–1071.

    Article  Google Scholar 

  9. Danesc C, Mizil N, Lee L. 2011. Chameleons in imagined conversations: a new approach to understanding coordination of linguistic style in dialogs. ACL HLT: 76–87.

  10. Singh D, Padgham L, Logan B. Integrating BDI agents with agent-based simulation platforms. AAMAS ’17 proceedings of the 16th conference on autonomous agents and multiagent systems; 2017. p. 249–250.

  11. Khawaja HS, Beg MO, Qamar S. Domain specific emotion lexicon expansion. 14th international conference on emerging technologies (ICET); 2018. p. 1–5.

  12. Li Y, Yang L, Xu B, Wang J, Lin H. Improving user attribute classification with text and social network attention. Cognitive Computation 2019;11(4):459–468.

    Article  Google Scholar 

  13. Lin J, Yu H, Miao C, Shen Z. An affective agent for studying composite emotions. Proceedings of the international conference on autonomous agents and multiagent systems; 2015. p. 1947–1948.

  14. Mohammad SM, Turney PD. Crowdsourcing a word-emotion association lexicon. Comput Intell 2013;29(3):436–465.

    Article  MathSciNet  Google Scholar 

  15. Mohammad S. Word affect intensities. Proceedings of the eleventh international conference on language resources and evaluation (LREC); 2018.

  16. Naeem B, Khan A, Beg M, Mujtaba H. A deep learning framework for clickbait detection on social area network using natural language cues. Journal of Computational Social Science 2020;3(1):231–243.

    Article  Google Scholar 

  17. Saunier J, Jones H. Mixed agent/social dynamics for emotion computation. Proceedings of the international conference on autonomous agents and multi-agent systems (AAMAS); 2014 . p. 645–652.

  18. Koźlak J, Zygmunt A, Nawarecki E. 2013. Modelling and analysing relations between entities using the multi-agents and social network approaches. arXiv:1303.6094.

  19. Thelwall M, Buckley K, Paltoglou G. Sentiment strength detection for the social web. Journal of the American Society for Information Science and Technology 2012;63(1):163–173.

    Article  Google Scholar 

  20. Shiota M, Campos B, Keltner D, Hertenstein MJ. Positive emotion and the regulation of interpersonal relationships. The regulation of emotion. Lawrence Erlbaum Associates; 2004 . p. 129–157.

  21. Smith ER, Mackie DM, Claypool Hm. 2014. Social psychology. Psychology Press.

  22. De Rivera J, Grinkis C. Emotions as social relationships. Motivation and Emotion 1986;10(4): 351–369.

    Article  Google Scholar 

  23. Gilovich T, Keltner D, Chen S, Nisbett RE. 2012. Social psychology. W.W. Norton and Company.

  24. Wang J, Xu M, Wang H, Zhang J. 2006. Classification of imbalanced data by using the SMOTE algorithm and locally linear embedding.

  25. Collenette J, Atkinson K, Bloembergen D, Tuyls K. Modelling mood in co-operative emotional agents. InDistributed Autonomous Robotic Systems 2018;6:559–572.

    Article  Google Scholar 

  26. Hortensius R, Hekele F, Cross ES. The perception of emotion in artificial agents. IEEE Transactions on Cognitive and Developmental Systems 2018;10(4):852–64.

    Article  Google Scholar 

  27. Wang Y, Huang M, Zhu X, Zhao L. Attention-based LSTM for aspect-level sentiment classification. Proceedings of the conference on empirical methods in natural language processing; 2016. p. 606–615.

  28. Yang HC, Lee CH, Wu CY. Sentiment discovery of social messages using self-organizing maps. Cognitive Computation 2018;10(6):1152–66.

    Article  Google Scholar 

  29. Cabrera-Diego LA, Bessis N, Korkontzelos I. Classifying emotions in stack overflow and JIRA using a multi-label approach. Knowledge-Based Systems 2020;195:105633.

    Article  Google Scholar 

  30. Calefato F, Lanubile F, Novielli N. Emotxt: a toolkit for emotion recognition from text. Seventh international conference on affective computing and intelligent interaction workshops and demos (ACIIW); 2017. p. 79–80.

  31. Danisman T, Alpkocak A. Feeler: emotion classification of text using vector space model; 2008. p. 53.

  32. Ho DT, Cao TH. A high-order hidden Markov model for emotion detection from textual data. Pacific rim knowledge acquisition workshop; 2012. p. 94–105.

  33. Islam MR, Zibran MF. DEVA: sensing emotions in the valence arousal space in software engineering text. Proceedings of the 33rd annual ACM symposium on applied computing; 2018 . p. 1536–1543.

  34. Awan MN, Beg MO. TOP-rank: a topicalpostionrank for extraction and classification of keyphrases in text. Computer Speech and Language 2020;65:101–116.

    Google Scholar 

  35. Neviarouskaya A, Prendinger H, Ishizuka M. Textual affect sensing for sociable and expressive online communication. Proceedings of the 2nd international conference on affective computing and intelligent interaction; 2007. p. 218–229.

  36. Strapparava C, Valitutti A. Wordnet affect: an affective extension of WordNet. Proceedings of the fourth international conference on language resources and evaluation (LREC’04); 2004. p. 1083–1086.

  37. Ma Y, Nguyen KL, Xing FZ, Cambria E. A survey on empathetic dialogue systems. Information Fusion 2020;64:50–70.

    Article  Google Scholar 

  38. Majumder N, Poria S, Hazarika D, Mihalcea R, Gelbukh A, Cambria E. Dialoguernn: an attentive rnn for emotion detection in conversations. Proceedings of the AAAI conference on artificial intelligence; 2019. p. 6818–6825.

  39. Wu S, Li Y, Zhang D, Zhou Y, Wu Z. Diverse and informative dialogue generation with context-specific commonsense knowledge awareness. Proceedings of the 58th annual meeting of the association for computational linguistics; 2020. p. 5811–5820.

  40. Wang Z, Ho SB, Cambria E. A review of emotion sensing: categorization models and algorithms. Multimedia Tools and Applications 2020;79(47):1–30.

    Google Scholar 

  41. Young T, Pandelea V, Poria S, Cambria E. Dialogue systems with audio context. Neurocomputing 2020;388:102–109.

    Article  Google Scholar 

  42. Liu Z, Shin J, Xu Y, Winata GI, Xu P, Madotto A, Fung P. Zero-shot cross-lingual dialogue systems with transferable latent variables. Proceedings of the conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP); 2019. p. 1297–1303.

Download references

Author information

Authors and Affiliations

  1. National University of Computer and Emerging Sciences, A. K. Brohi Road, H-11/4, Islamabad, Pakistan

    Saira Qamar, Hasan Mujtaba, Hammad Majeed & Mirza Omer Beg

Authors
  1. Saira Qamar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hasan Mujtaba
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hammad Majeed
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mirza Omer Beg
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mirza Omer Beg.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Additional information

Informed Consent

No human or animal subjects were used in the study.

Research involving human participants and/or animals

This article does not contain any studies with human participants or animals performed by any of the authors.

Publisher’s Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Qamar, S., Mujtaba, H., Majeed, H. et al. Relationship Identification Between Conversational Agents Using Emotion Analysis. Cogn Comput 13, 673–687 (2021). https://doi.org/10.1007/s12559-020-09806-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-020-09806-5

Keywords

Search

Navigation