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Chatbots for CRM and Dialogue Management Read chapter Read first chapter

2021 | OriginalPaper | Chapter

3. Adjusting Chatbot Conversation to User Personality and Mood

Author : Boris Galitsky

Published in: Artificial Intelligence for Customer Relationship Management

Publisher: Springer International Publishing

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Abstract

As conversational CRM systems communicate with human customers and not other computer systems, they need to tackle human emotions in a way to optimize the outcome of a chatbot session. A chatbot needs to understand the emotions of its peers and produce utterances which not only match their emotional states but also attempt to improve it towards solving a problem. We construct a model and a training dataset of emotions and personality from various sources to properly react to the customer in the emotional space and to navigate him through it. We evaluated an overall contribution of a chatbot enabled with affective computing and observed up to 18% boost in the relevance of responses, as perceived by customers.

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previous chapter Recommendation by Joining a Human Conversation
next chapter A Virtual Social Promotion Chatbot with Persuasion and Rhetorical Coordination
Literature
Alm CO, Roth D, Sproat R (2005) Emotions from text: machine learning for text-based emotion prediction. In Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing (HLT ‘05). Association for Computational Linguistics, Stroudsburg, PA, USA, pp 579–586
Arnold MB (1960) Emotion and personality. Columbia University Press, New York
Balahur A, Jesus MH, Andres M, Rafael M (2011) EmotiNet: a knowledge base for emotion detection in text built on the appraisal theories. Springer Berlin Heidelberg, pp 27–39
Bechara A (2004) The role of emotion in decision-making: evidence from neurological patients with orbitofrontal damage. Brain Cogn 55(1):30–40. Development of Orbitofrontal Function
Blake R, Lillicrap T, Beaudoin P, Bengio Y, Bogacz R, Christensen A, Clopath C, Costa R, Berker A, Ganguli S, Gillon C, Hafner D, Kepecs A, Kriegeskorte N, Latham P, Lindsay G, Miller K, Naud R, Pack C, Kording K (2019) A deep learning framework for neuroscience. Nat Neurosci 22:1761–1770CrossRef
Boehner K, Vertesi J, Sengers P, Dourish P (2007) How HCI interprets the probes. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM Press, New York, pp 1077–1086
Bratman ME (1987) Intention, plans and practical reason. Harvard University Press, Cambridge, MA
Burleson W (2006) Affective learning companions: strategies for empathetic agents with real-time multimodal affective sensing to foster meta-cognitive and meta-affective approaches to learning, motivation, and perseverance. Thesis for the degree of Doctor of Philosophy
Cassell J, Bickmore T (2003) Negotiated collusion: modeling social language and its relationship effects in intelligent agents. User Model User-Adap Inter 13:89–132CrossRef
Chapanond A, Krishnamoorthy MS, Yener B (2005) Graph theoretic and spectral analysis of enron email data. Comput Math Org Theory 11(3):265–281CrossRef
Chklovski T, Pantel P (2004) Verbocean: mining the web for fine-grained semantic verb relations. EMNLP 4:33–40
Cochran RE, Lee FJ, Chown E (2006) Modeling emotion: Arousal’s impact on memory. In: Proceedings of the 28th annual conference of the cognitive science society, pp 1133–1138
Colombetti G (2009) From affect programs to dynamical discrete emotions. Philos Psychol 22(4):407–425CrossRef
Dalgleish T, Mick P (2000) Handbook of cognition and emotion. John Wiley & Sons
Damasio A (2004) Descartes’ Error: Emotion, Reason, and the Human Brain. Penguin Books
Danescu C, Sudhof M, Jurafsky D, Leskovec J, Potts C (2013) A computational approach to politeness with application to social factors. In: Proc. of ACL
de Rivera J (ed) (1981) Conceptual encounter: a method for the exploration of human experience. University Press of America, Washington, DC
Diesner J, Craig SE (2015) Little bad concerns: using sentiment analysis to assess structural balance in communication networks. In: Advances in Social Networks Analysis and Mining, IEEE/ACM International Conference on. IEEE, pp 342–348
Dunbar RI (1998) The social brain hypothesis. Brain 9:178–190
Ekman P (1992) An argument for basic emotions. Cogn Emot 6(3–4):169–200CrossRef
Evertsz R, Pedrotti M, Busetta P, Acar H, Ritter FE (2009) Populating VBS2 with realistic virtual actors. In: Proceedings of the 18th conference on behavior representation in modeling and simulation
Galitsky B (2001) Learning the axiomatic reasoning about mental states assists the emotional development of the autistic patients. AAAI FSS-2001 symposium on emotional and intelligent II, Cape Cod, MA
Galitsky B (2002) Extending the BDI model to accelerate the mental development of autistic patients. In: Second International Conference on Development & Learning. Cambridge, MA
Galitsky B (2003) Using mental simulator for emotional rehabilitation of autistic patients. FLAIRS conference pp 166–171
Galitsky B (2005) On a distance learning rehabilitation of autistic reasoning. In: Encyclopedia of online learning and technologies, vol 4. Idea Publishing Group
Galitsky B (2015) Recognizing intent and trust of a facebook friend to facilitate autonomous conversation. Workshops at the Twenty-Ninth AAAI Conference on Artificial Intelligence
Galitsky B (2016a) Theory of mind engine. In: Computational Autism. Springer, Cham
Galitsky B (2016b) Rehabilitating Autistic Reasoning. In: Computational Autism. Springer, Cham
Galitsky B (2019) Enabling a bot with understanding argumentation and providing arguments. In: Developing enterprise chatbots. Springer, Cham, Switzerland.
Galitsky B (2020) Utilizing discourse structure of noisy user-generated content for chatbot learning. US Patent 10,599,885
Galitsky B, Shpitsberg I (2015) Evaluating assistance to individuals with autism in reasoning about mental world. Artificial intelligence applied to assistive technologies and smart environments: Papers from the 2015 AAAI Workshop
Galitsky B, EW McKenna (2017) Sentiment extraction from consumer reviews for providing product recommendations. US Patent 9,646,078.
Galitsky B, Tumarkina I (2004) Justification of customer complaints using emotional states and mental actions. FLAIRS conference, Miami, Florida
Galitsky B, Kovalerchuk B (2006) Mining the blogosphere for contributor’s sentiments. AAAI Spring symposium on analyzing weblogs, Stanford, CA
Galitsky B, Kuznetsov SO, Samokhin MV (2005) Analyzing conflicts with concept-based learning. International conference on conceptual structures, pp 307–322
Galitsky B, Chen H, Du S (2009) Inverting semantic structure of customer opinions expressed in forums and blogs. In: 17th International Conference on Conceptual Structures, Suppl. Proc.
Galitsky B, Dobrocsi G, de la Rosa JL (2010) Inverting semantic structure under open domain opinion mining twenty-third international FLAIRS conference
Galitsky B, Ilvovsky D, Lebedeva N, Usikov D (2014) Improving trust in automation of social promotion. AAAI Spring Symposium Series
Hudlicka E (2016) Computational analytical framework for affective modeling: towards guidelines for designing. In: Psychology and mental health: concepts, methodologies, tools, and applications: concepts, methodologies, tools, and applications, pp 1–64
Ickes WJ (ed) (1997) Empathic accuracy. Guilford Press, New York
Jain S, Asawa K (2015) EMIA: emotion model for intelligent agent. J Intell Syst 24(4):449–465CrossRef
Kaur J, Saini J (2014) Emotion detection and sentiment analysis in text corpus: a differential study with informal and formal writing styles. Int J Comput Appl 101:1–9
Kovalerchuk B, Kovalerchuk M (2017) Toward virtual data scientist with visual means. In: IJCNN
Lerner J, Keltner D (2000) Beyond valence: toward a model of emotion-specific influences on judgment and choice. Cogn Emot 14:473–493CrossRef
Mairesse F, Marilyn AW, Matthias RM, Roger KM (2007) Using linguistic cues for the automatic recognition of personality in conversation and text. J Artif Int Res 30, 1(November 2007):457–500
Makhalova T, Ilvovsky D, Galitsky B (2019) Information retrieval chatbots based on conceptual models. International Conference on Conceptual Structures, pp 230–238
Marinier RP, Laird JE (2004) Toward a comprehensive computational model of emotions and feelings. In: Proceedings of sixth international conference on cognitive modeling: ICCM, pp 172–177
Melchers MC, Li M, Haas BW, Reuter M, Bischoff L and Montag C (2016). Similar Personality patterns are associated with empathy in four different countries. Frontiers in Psychology. Retrieved from http://​journal.​frontiersin.​org/​article/​https://​doi.​org/​10.​3389/​fpsyg.​2016.​00290/​full
Miller DA, Cronin T, Garcia AL, Branscombe NR (2009) The relative impact of anger and efficacy on collective action is affected by feelings of fear. Group Proc Int Relat 12(4):445–462CrossRef
Minsky M (2006) The emotion machine. Simon & Schuster, New York
Oberlander J, Nowson S (2006) Whose thumb is it anyway? classifying author personality from weblog text. In: Proc. of the 44th Annual Meeting of the Association for Computational Linguistics ACL, pp 627–634
Ortu M, Adams B, Destefanis G, Tourani P, Marchesi M, Tonelli R (2015) Are bullies more productive?: an empirical study of affectiveness vs. vs. issue fixing time. In: Proc. of MSR ‘15. IEEE Press, pp 303–313
Pantel P, Pennacchiotti M (2006) Espresso: leveraging generic patterns for automatically harvesting semantic relations. In: Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics, ACL-44, pages 113–120, Stroudsburg, PA, USA
Pascual-Leone A, Greenberg LS (2006) Insight and awareness in experiential therapy. In: Castonguay LG, Hill CE (eds) Insight in psychotherapy. American Psychological Association, Washington, DC, pp 31–56
Pascual-Leone A, Greenberg LS (2007) Emotional processing in experiential therapy: why “the only way out is through” . J Consult Clin Psychol 75(6):875–887CrossRef
Perlin K (1997) Layered compositing of facial expression. ACM SIGGRAPH 97.
Peterson K, Hohensee M and Xia F (2011) Email formality in the workplace: a case study on the Enron corpus. In: Proceedings of the Workshop on Languages in Social Media, pp 86–95
Picard RW (1997) Affective computing. MIT Press, Cambridge, MA
Plutchik R (1984) Emotions: a general psychoevolutionary theory. Approac Emot 1984:197–219
Qiu G, He X, Zhang F, Shi Y, Jiajun Bu, Chen C (2010) DASA: dissatisfaction-oriented Advertising based on sentiment analysis. Expert Syst Appl 37(9):6182–6191CrossRef
Rangel F, Rosso P (2016) On the impact of emotions on author profiling. Inf Process Manage 52(1):73–92CrossRef
Richards BA, Lillicrap TP, Beaudoin P, Bengio Y, Bogacz R, Christensen A, Clopath C, Costa R, Berker A, Ganguli S, Gillon C, Hafner D, Kepecs A, Kriegeskorte N, Latham P, Lindsay G, Miller K, Naud R, Pack C, Kording K (2019) A deep learning framework for neuroscience. Nat Neurosci 22:1761–1770
Russell JA (1980) A circumplex model of affect. J Person Soc Psychol 39(6):1161
Schiller MRG, Gobet FR (2012) A comparison between cognitive and AI models of blackjack strategy learning. Lect Notes Comput Sci, pp 143–155
Schröder M (2010) The SEMAINE API: towards a standards-based framework for building emotion-oriented systems. Adv Hum Comput Interact 2010:319–406CrossRef
Seki Y, Koji E, Noriko K, Masaki A (2005) Multi-document summarization with subjectivity analysis at duc 2005. In: Proceedings of the Document Understanding Conference (DUC)
Shaver P, Schwartz J, Kirson D, O’Connor C (1987) Emotion knowledge: further exploration of a prototype approach. J Pers Soc Psychol 52(6):1061–1086CrossRef
Sloman A (2000) Architecture-based conceptions of mind. In: Proceedings of the 11th international congress of logic, methodology and philosophy of science. Kluwer, Dordrecht, p 397
Somasundaran S, Theresa W, Janyce W, Veselin S (2007) Q/a with attitude: exploiting opinion type analysis for improving question answering in on-line discussions and the news. In ICWSM
Strapparava C, Mihalcea RF (2007) SemEval ‘07: Proceedings of the 4th International Workshop on Semantic Evaluations, pp 70–74
Strapparava C, Valitutti A (2004) WordNet-Affect: an affective extension of WordNet. In: Proc. of LREC, pp 1083–1086
Thelwall M, Buckley K, Paltoglou G (2012) Sentiment strength detection for the social web. J Am Soc Inf Sci Technol 63(1):163–173
Terveen L, Hill W, Amento B, McDonald D and Creter J (1997) PHOAKS: a system for sharing recommendations. Commun ACM 40:59–62
Trampe D, Quoidbach J, Taquet M (2015) Emotions in everyday life. PLoS ONE 10:e0145450CrossRef
Viterbi AJ (1967) Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Trans Inf Theory 13(2):260–269CrossRef
Vu HT, Graham N, Sakriani S, Tomoki T, Satoshi N (2014) Acquiring a dictionary of emotion-provoking events. In: EACL, pp 128–132
Wang N, Johnson WL, Mayer RE, Rizzo P, Shaw E, Collins H (2005) Thepoliteness effect: pedagogical agents and learning gains. Front Artific Intel-ligence Appli 125:686–693
Wilson JR, Forbus KD, McLure MD (2013) Am I really scared? A multi-phase computational model of emotions. In: Proceedings of the second annual conference on advances in cognitive systems, pp 289–304
Wilt J, Funkhouser K, Revelle W (2011) The dynamic relationships of affective synchrony to perceptions of situations. J Res Pers 45:309–321CrossRef
Metadata
Title
Adjusting Chatbot Conversation to User Personality and Mood
Author
Boris Galitsky
Copyright Year
2021
Book
Artificial Intelligence for Customer Relationship Management

Print ISBN: 978-3-030-61640-3

Electronic ISBN: 978-3-030-61641-0

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-61641-0_3