Adam Miner
Amanda Chow
Andreas Paepcke
ABSTRACT
A study deployed the mental health Relational Frame Theory as grounding for an analysis of sentiment dynamics in human-language dialogs. The work takes a step towards enabling use of conversational agents in mental health settings. Sentiment tendencies and mirroring behaviors in 11k human-human dialogs were compared with behaviors when humans interacted with conversational agents in a similar-sized collection. The study finds that human sentiment-related interaction norms persist in human-agent dialogs, but that humans are twice as likely to respond negatively when faced with a negative utterance by a robot than in a comparable situation with humans. Similarly, inhibition towards use of obscenity is greatly reduced. We introduce a new Affective Neural Net implementation that specializes in analyzing sentiment in real time.
- Tim Althoff, Kevin Clark, and Jure Leskovec. 2016. Natural Language Processing for Mental Health: Large Scale Discourse Analysis of Counseling Conversations. Transactions of the Association for Computational Linguistics (2016).Google Scholar
- Timothy Bickmore, Amanda Gruber, and Rosalind Picard. 2005. Establishing the computer-patient working alliance in automated health behavior change interventions. Patient education and counseling 59, 1 (2005), 21--30.Google Scholar
- Timothy W Bickmore, Daniel Schulman, and Candace Sidner. 2013. Automated interventions for multiple health behaviors using conversational agents. Patient education and counseling 92, 2 (2013), 142--148.Google Scholar
- Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. arxiv.org (12 2014). http://arxiv.org/abs/1412.3555Google Scholar
- Christopher Cieri and et al. 2004/2005. Fisher English Training Speech Part 1/2 Transcripts LDC2004T19. Linguistic Data Consortium. (2004/2005). https://catalog.ldc.upenn.edu/LDC2004T19Google Scholar
- Rik Crutzen, Gjalt-Jorn Y Peters, Sarah Dias Portugal, Erwin M Fisser, and Jorne J Grolleman. 2011. An artificially intelligent chat agent that answers adolescents' questions related to sex, drugs, and alcohol: an exploratory study. Journal of Adolescent Health 48, 5 (2011), 514--519.Google ScholarCross Ref
- Alison M Darcy, Alan K Louie, and Laura Weiss Roberts. 2016. Machine Learning and the Profession of Medicine. JAMA 315, 6 (2016), 551--552.Google ScholarCross Ref
- Xiaowen Ding and Bing Liu. 2007. The Utility of Linguistic Rules in Opinion Mining. In Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 811--812. Google ScholarDigital Library
- David Daniel Ebert, Anna-Carlotta Zarski, Helen Christensen, Yvonne Stikkelbroek, Pim Cuijpers, Matthias Berking, and Heleen Riper. 2015. Internet and computer-based cognitive behavioral therapy for anxiety and depression in youth: a meta-analysis of randomized controlled outcome trials. PloS one 10, 3 (2015), e0119895.Google ScholarCross Ref
- Paul Ekman. 1984. Expression and the nature of emotion. Approaches to emotion 3 (1984), 19--344.Google Scholar
- Yarin Gal. 2015. A Theoretically Grounded Application of Dropout in Recurrent Neural Networks. arxiv.org (12 2015). http://arxiv.org/abs/1512.05287Google Scholar
- Felix Greaves, Daniel Ramirez-Cano, Christopher Millett, Ara Darzi, and Liam Donaldson. 2013. Harnessing the cloud of patient experience: using social media to detect poor quality healthcare. BMJ quality & safety (2013), bmjqs'2012.Google Scholar
- David E Greenway, Emily K Sandoz, and David R Perkins. 2010. Potential applications of relational frame theory to natural language systems. In Fuzzy Systems and Knowledge Discovery (FSKD), 2010 Seventh International Conference on, Vol. 6. IEEE, 2955--2958.Google ScholarCross Ref
- Steven C Hayes, Dermot Barnes-Holmes, and Bryan Roche. 2001. Relational frame theory: A post-Skinnerian account of human language and cognition. Springer Science & Business Media.Google Scholar
- Jing Huang, Qi Li, Yuanyuan Xue, Taoran Cheng, Shuangqing Xu, Jia Jia, and Ling Feng. 2015. Teenchat: a chatterbot system for sensing and releasing adolescents stress. In Health Information Science. Springer, 133--145.Google Scholar
- Eva Hudlicka. 2003. To feel or not to feel: The role of affect in human-computer interaction. International journal of human-computer studies 59, 1 (2003), 1--32. Google ScholarDigital Library
- C.J. Hutto and Eric Gilbert. 2014. VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text. In Proceedings of the Eighth International AAAI Conference on Weblogs and Social Media. AAAI Publications, Ann Arbor, MI, 216--225.Google Scholar
- Kristin N Javaras, Nan M Laird, Ted Reichborn-Kjennerud, Cynthia M Bulik, Harrison G Pope, and James I Hudson. 2008. Familiality and heritability of binge eating disorder: results of a case-control family study and a twin study. International Journal of Eating Disorders 41, 2 (2008), 174--179.Google ScholarCross Ref
- Nitin Jindal and Bing Liu. 2006. Identifying Comparative Sentences in Text Documents. In Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '06). ACM, New York, NY, USA, 244--251. DOI: http://dx.doi.org/10.1145/1148170.1148215 Google ScholarDigital Library
- David Kessler, Glyn Lewis, Surinder Kaur, Nicola Wiles, Michael King, Scott Weich, Debbie J Sharp, Ricardo Araya, Sandra Hollinghurst, and Tim J Peters. 2009. Therapist-delivered internet psychotherapy for depression in primary care: a randomised controlled trial. The Lancet 374, 9690 (2009), 628--634.Google Scholar
- Adam DI Kramer, Jamie E Guillory, and Jeffrey T Hancock. 2014. Experimental evidence of massive-scale emotional contagion through social networks. Proceedings of the National Academy of Sciences 111, 24 (2014), 8788--8790.Google ScholarCross Ref
- Yang Liu, Xiaohui Yu, Zhongshuai Chen, and Bing Liu. 2013. Sentiment Analysis of Sentences with Modalities. In Proceedings of the 2013 International Workshop on Mining Unstructured Big Data Using Natural Language Processing (UnstructureNLP '13). ACM, New York, NY, USA, 39--44. DOI: http://dx.doi.org/10.1145/2513549.2513556 Google ScholarDigital Library
- Ramesh Manuvinakurike, Wayne F Velicer, and Timothy W Bickmore. 2014. Automated indexing of Internet stories for health behavior change: weight loss attitude pilot study. Journal of medical Internet research 16, 12 (2014).Google ScholarCross Ref
- Evan Mayo-Wilson and Paul Montgomery. 2013. Media-delivered cognitive behavioural therapy and behavioural therapy (self-help) for anxiety disorders in adults. Cochrane Database Syst Rev 9 (2013), CD005330.Google Scholar
- Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013. Distributed Representations of Words and Phrases and their Compositionality. In Advances in Neural Information Processing Systems 26, C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Q. Weinberger (Eds.). Curran Associates, Inc., 3111--3119. http://arxiv.org/abs/1310.4546 Google ScholarDigital Library
- Adam S Miner, Arnold Milstein, Stephen Schueller, Roshini Hegde, Christina Mangurian, and Eleni Linos. 2016. Smartphone-based conversational agents and responses to questions about mental health, interpersonal violence, and physical health. JAMA internal medicine (2016).Google Scholar
- David C Mohr, Michelle Nicole Burns, Stephen M Schueller, Gregory Clarke, and Michael Klinkman. 2013. Behavioral intervention technologies: evidence review and recommendations for future research in mental health. General hospital psychiatry 35, 4 (2013), 332--338.Google Scholar
- Theresa B Moyers and William R Miller. 2013. Is low therapist empathy toxic? Psychology of Addictive Behaviors 27, 3 (2013), 878.Google ScholarCross Ref
- James W Pennebaker, Ryan L Boyd, Kayla Jordan, and Kate Blackburn. 2015. The Development and Psychometric Properties of LIWC2015. UT Faculty/Researcher Works (2015).Google Scholar
- James W Pennebaker, Matthias R Mehl, and Kate G Niederhoffer. 2003. Psychological aspects of natural language use: Our words, our selves. Annual review of psychology 54, 1 (2003), 547--577.Google Scholar
- Erik Rautalinko, Hans-Olof Lisper, and Bo Ekehammar. 2007. Reflective listening in counseling: effects of training time and evaluator social skills. American journal of psychotherapy 61, 2 (2007), 191.Google Scholar
- Byron Reeves and Cliff Nass. 1996. The Media Equation: How People Treat Computers, Television, and New Media Like Real People and Places. Cambridge University Press, NY. Google ScholarDigital Library
- Rainer Reisenzein, Eva Hudlicka, Mehdi Dastani, Jonathan Gratch, Koen Hindriks, Emiliano Lorini, and John-Jules Ch Meyer. 2013. Computational modeling of emotion: Toward improving the inter-and intradisciplinary exchange. Affective Computing, IEEE Transactions on 4, 3 (2013), 246--266. Google ScholarDigital Library
- Paul Tero, Ilia Zaitsev, and Rollo Carpenter. 2016. Cleverbot Data for Machine Learning. http://www.existor.com/en/ml-cleverbot-data-for-machine-learning.html. (January 2016).Google Scholar
- Adam Waytz, John Cacioppo, and Nicholas Epley. 2010. Who sees human? The stability and importance of individual differences in anthropomorphism. Perspectives on Psychological Science 5, 3 (2010), 219--232.Google ScholarCross Ref
Index Terms
- Conversational Agents and Mental Health: Theory-Informed Assessment of Language and Affect
Recommendations
Conversational Agents: Acting on the Wave of Research and Development
CHI EA '19: Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing SystemsIn the last five years, work on software that interacts with people via typed or spoken natural language, called chatbots, intelligent assistants, social bots, virtual companions, non-human players, and so on, increased dramatically. Chatbots burst into ...
Assessment with computer agents that engage in conversational dialogues and trialogues with learners
This article describes conversation-based assessments with computer agents that interact with humans through chat, talking heads, or embodied animated avatars. Some of these agents perform actions, interact with multimedia, hold conversations with ...
Affect as Information about Users' Attitudes to Conversational Agents
WI-IAT '08: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03This paper presents a novel method for automatic evaluation of conversational agents. In the method, information about users’ attitudes and sentiments to conversational agents and their performance are achieved by analyzing their general emotional ...
Comments