Skip to main content
×
Home

Natural language processing in mental health applications using non-clinical texts

  • RAFAEL A. CALVO (a1), DAVID N. MILNE (a1), M. SAZZAD HUSSAIN (a1) (a2) and HELEN CHRISTENSEN (a3)
Abstract
Abstract

Natural language processing (NLP) techniques can be used to make inferences about peoples’ mental states from what they write on Facebook, Twitter and other social media. These inferences can then be used to create online pathways to direct people to health information and assistance and also to generate personalized interventions. Regrettably, the computational methods used to collect, process and utilize online writing data, as well as the evaluations of these techniques, are still dispersed in the literature. This paper provides a taxonomy of data sources and techniques that have been used for mental health support and intervention. Specifically, we review how social media and other data sources have been used to detect emotions and identify people who may be in need of psychological assistance; the computational techniques used in labeling and diagnosis; and finally, we discuss ways to generate and personalize mental health interventions. The overarching aim of this scoping review is to highlight areas of research where NLP has been applied in the mental health literature and to help develop a common language that draws together the fields of mental health, human-computer interaction and NLP.

  • View HTML
    • Send article to Kindle

      To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle.

      Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

      Find out more about the Kindle Personal Document Service.

      Natural language processing in mental health applications using non-clinical texts
      Available formats
      ×
      Send article to Dropbox

      To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your Dropbox account. Find out more about sending content to Dropbox.

      Natural language processing in mental health applications using non-clinical texts
      Available formats
      ×
      Send article to Google Drive

      To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your Google Drive account. Find out more about sending content to Google Drive.

      Natural language processing in mental health applications using non-clinical texts
      Available formats
      ×
Copyright
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Footnotes
Hide All

RAC and SH are supported by the Young and Well Cooperative Research Centre, which is established under the Australian Government’s Cooperative Research Centres Program. RAC is supported by an Australian Research Council Future Fellowship FT140100824. RAC and DM are supported by an Australian Research Council Linkage Project. HC is supported by an NHMRC Fellowship 1056964.

Footnotes
References
Hide All
Abbe A., Grouin C., Zweigenbaum P., and Falissard B. 2015. Text mining applications in psychiatry: a systematic literature review. International Journal of Methods in Psychiatric Research 25 (2): 86100.
Aguilera A., and Muench F. 2012. There’s an App for that: information technology applications for cognitive behavioral practitioners. The Behavior Therapist/AABT 35 (4): 6573.
Aguilera A., and Muñoz R. F. 2011. Text messaging as an adjunct to CBT in low-income populations: a usability and feasibility pilot study. Professional Psychology: Research and Practice 42 (6): 472–8.
American College Health Association 2009. National college health assessment spring 2008 reference group data report. Journal of American College Health 57, 477–88.
Armstrong R., Hall B. J., Doyle J., and Waters E. 2011. ‘Scoping the scope’ of a cochrane review. Journal of Public Health 33 (1): 147150.
Barak A. 2007. Emotional support and suicide prevention through the internet: a field project report. Computers in Human Behavior 23 (2): 971984.
Barak A., Boneh O., and Dolev-Cohen M. 2010. Factors underlying participants’ gains in online support groups. In Blachnio A., Przepiorka A. and Rowiński T. (eds.), Internet in psychological research, Warsaw, Poland: Cardinal Stefan Wyszyński University Press, pp. 1347.
Barak A., and Grohol J. M. 2011. Current and future trends in internet-supported mental health interventions. Journal of Technology in Human Services 29 (3): 155196.
Barak A., Hen L., Boniel-Nissim M., and Shapira N. 2008. A comprehensive review and a meta-analysis of the effectiveness of internet-based psychotherapeutic interventions. Journal of Technology in Human Services 26 (2–4): 109160.
Barak A., and Miron O. 2005. Writing characteristics of suicidal people on the Internet: a psychological investigation of emerging social environments. Suicide and Life-Threatening Behavior 35 (5): 507524.
Bauer S., Percevic R., Okon E., Meermann R. U., and Kordy H. 2003. Use of text messaging in the aftercare of patients with bulimia nervosa. European Eating Disorders Review 11 (3): 279290.
Bental D., and Cawsey A. 2002. Personalized and adaptive systems for medical consumer applications. Communications of the ACM 45 (5): 6263.
Bental D. S., Cawsey A., and Jones R. 1999. Patient information systems that tailor to the individual. Patient Education and Counseling 36, 171180.
Bewick B. M., Trusler K., Barkham M., Hill A. J., Cahill J., and Mulhern B. 2008. The effectiveness of web-based interventions designed to decrease alcohol consumption-a systematic review. Preventive Medicine 47 (1): 1726.
Bickmore T., and Gruber A. 2010. Relational agents in clinical psychiatry. Harvard Review of Psychiatry 18 (2): 119130.
Bickmore T., Gruber A., and Picard R. 2005. Establishing the computer-patient working alliance in automated health behavior change interventions. Patient Education and Counseling 59 (1): 2130.
Bickmore T., and Mauer D. 2006. Modalities for building relationships with handheld computer agents. In Proceedings of the CHI’06 Extended Abstracts on Human Factors in Computing Systems - CHI EA’06, New York, pp. 544–549.
Bickmore T., Schulman D., and Yin L. 2010. Maintaining engagement in long-term interventions with relational agents. Applied Artificial Intelligence 24 (6): 648666.
Bickmore T. W., and Picard R. W. 2005. Establishing and maintaining long-term human-computer relationships. ACM Transactions on Computer-Human Interaction (TOCHI) 12 (2): 293327.
Binstead K., Cawsey A., and Jones R., 1995. Generating personalised information using the medical record. In Proceedings of Artificial Intelligence In Medicine, Berlin, New York, pp. 2941.
Bohannon J. 2015. The synthetic therapist. Science 349 (6245): 250251.
Bradley M. M., and Lang P. J. 1999. Affective norms for english words (anew): instruction manual and affective ratings. Technical Report C-1, The Center for Research in Psychophysiology, University of Florida.
Brew C., 2016. Classifying reachout posts with a radial basis function svm. In Proceedings of the 3rd Workshop on Computational Linguistics and Clinical Psychology, San Diego, CA, USA, pp. 138142.
Buchanan B. G., Moore J. D., Forsythe D. E., Carenini G., Ohlsson S., and Banks G. 1995. An intelligent interactive system for delivering individualized information to patients. Artificial Intelligence in Medicine 7 (2): 117154.
Calvo R., and D’Mello S. 2010. Affect detection: an interdisciplinary review of models, methods, and their applications. IEEE Transactions on Affective Computing 1 (1): 1837.
Calvo R. A., Dinakar K., Picard R., and Maes P., 2016. Computing in mental health. In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems, Santa Clara, California, USA, pp. 34383445.
Calvo R. A., and Kim S. 2013. Emotions in text: dimensional and categorical models. Computational Intelligence 29 (3): 527543.
Calvo R. A., Peters D., and D’Mello S. 2015. When technologies manipulate our emotions. Communications of the ACM 58 (11): 4142.
Cherry C., Mohammad S. M., and De Bruijn B. 2012. Binary classifiers and latent sequence models for emotion detection in suicide notes. Biomedical Informatics Insights 5 (Suppl 1): 147154.
Christensen H., Batterham P., Mackinnon A., Griffiths K. M., Hehir K. K., Kenardy J., Gosling J., and Bennett K. 2014. Prevention of generalized anxiety disorder using a web intervention, iChill: randomized controlled trial. Journal of Medical Internet Research 16 (9): e199.
Christensen H., Griffiths K. M., and Jorm A. F. 2004. Delivering interventions for depression by using the internet: randomised controlled trial. BMJ 328 (7434): 265268.
Chung C., and Pennebaker J. W. 2007. The psychological function of function words. In Fiedler K. (ed.), Social communication, New York: Psychology Press, pp. 343359.
Coch J., 1996. Evaluating and comparing three text-production techniques. In Proceedings of the 16th Conference on Computational Linguistics, vol. 1. Stroudsburg, PA, USA, pp. 249254.
Coppersmith G., Dredze M., Harman C., Hollingshead K., and Mitchell M., 2015. Clpsych 2015 shared task: depression and ptsd on twitter. In Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, Denver, Colorado, pp. 3139.
Coviello L., Sohn Y., Kramer A. D., Marlow C., Franceschetti M., Christakis N. A., and Fowler J. H. 2014. Detecting emotional contagion in massive social networks. PloS One 9 (3): e90315.
De Carolis B., de Rosis F., Grasso F., Rossiello A., Berry D. C., and Gillie T. 1996. Generating recipient-centered explanations about drug prescription. Artificial Intelligence in Medicine 8 (2): 123145.
De Choudhury M., and Counts S. 2014. Characterizing and predicting postpartum depression from shared facebook data. Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work and Social Computing, New York, NY, USA, pp. 626638.
De Choudhury M., Counts S., and Horvitz E., 2013a. Predicting postpartum changes in emotion and behavior via social media. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, New York, NY, USA, pp. 32673276.
De Choudhury M., Counts S., and Horvitz E. 2013b. Social Media As a Measurement Tool of Depression in Populations. In Proceedings of the 5th Annual ACM Web Science Conference (WebSci ’13). New York, NY, USA: ACM, pp. 4756.
De Choudhury M., and De S., 2014. Mental health discourse on reddit: self-disclosure, social support, and anonymity. In The International AAAI Conference on Weblogs and Social Media (ICWSM), Ann Arbor, MI, USA, pp. 7180.
De Choudhury M., Gamon M., Counts S., and Horvitz E., 2013. Predicting depression via social media. In The International AAAI Conference on Weblogs and Social Media (ICWSM), Boston, USA, pp. 128137.
DiMarco C., Covvey H., Cowan D., DiCiccio V., Hovy E., Lipa J., and Mulholland D., 2007. The development of a natural language generation system for personalized e-health information. In Medinfo 2007: Proceedings of the 12th World Congress on Health (Medical) Informatics; Building Sustainable Health Systems, Amsterdam, Netherlands, pp. 23392344.
Dinakar K., Chaney A. J. B., Lieberman H., and Blei D. M. 2014. Real-time topic models for crisis counseling. In Proceedings of the 20th ACM Conference on Knowledge Discovery and Data Mining, Data Science for the Social Good Workshop, New York, USA.
D’Mello S. K., Lehman B., and Graesser A. 2011. A Motivationally Supportive Affect-Sensitive AutoTutor. Inbook. In Calvo R. A. and D’Mello S. K. (eds.), New Perspectives on Affect and Learning Technologies, Springer New York, pp. 113126.
Dockrey M. 2007. Emulating mental state in natural language generation systems. Technical Report, University of British Columbia.
Dodds P. S., Harris K. D., Kloumann I. M., Bliss C. A., and Danforth C. M. 2011. Temporal patterns of happiness and information in a global social network: hedonometrics and twitter. PloS One 6 (12): e26752.
Donker T., Griffiths K. M., Cuijpers P., and Christensen H. 2009. Psychoeducation for depression, anxiety and psychological distress: a meta-analysis. BMC Medicine 7 (1): e79.
Dowling M., and Rickwood D. 2013. Online counseling and therapy for mental health problems: a systematic review of individual synchronous interventions using chat. Journal of Technology in Human Services 31 (1): 121.
Durkheim E., 1897. Suicide: A Study in Sociology. [1951] Glencoe, Illinois: Free Press.
Eysenbach G., Powell J., Englesakis M., Rizo C., and Stern A. 2004. Health related virtual communities and electronic support groups: systematic review of the effects of online peer to peer interactions. BMJ 328 (7449): 11661172.
Finn J., and Bruce S. 2008. The LivePerson model for delivery of etherapy services: a case study. Journal of Technology in Human Services 26 (2–4): 282309.
Forman G. 2003. An extensive empirical study of feature selection metrics for text classification. The Journal of machine learning research 3; 12891305.
Gatt A., Portet F., Reiter E., Hunter J., Mahamood S., Moncur W., and Sripada S. 2009. From data to text in the neonatal intensive care unit: using NLG technology for decision support and information management. A.I. Communications 22 (3): 153186.
Golder S., and Macy M. 2011. Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures. Science 333 (6051): 18781881.
Griffiths M. K., Calear L. A., and Banfield M. 2009. Systematic review on internet support groups (ISGs) and depression (1): do ISGs reduce depressive symptoms? Journal of Medical Internet Research 11 (3): e40.
Grohol J. M. 2004. Online counseling: a historical perspective. In Kraus R., Stricker G., and Speyer C. (eds.), Online Counseling: A Handbook for Mental Health Professionals, San Diego, CA: Elsevier Academic Press, pp. 5168.
Grohol J. M. 2010. Using websites, blogs and wikis in mental health. In Anthony K., Nagel D. A. N., and Goss S. (eds.), The use of technology in mental health applications ethics and practice, Springfield, IL: Charles C. Thomas, pp. 6875.
Haw C., Hawton K., Niedzwiedz C., and Platt S. 2013. Suicide clusters: a review of risk factors and mechanisms. Suicide and Life-Threatening Behavior 43 (1): 97108.
He Q., Veldkamp B. P., Glas C. A., and de Vries T. 2015. Automated assessment of patients’ self-narratives for posttraumatic stress disorder screening using natural language processing and text mining. Assessment 0 (0): 116. SAGE Publications.
Hirschberg J., and Manning C. D. 2015. Advances in natural language processing. Science 349 (6245): 261266.
Homan C. M., Johar R., Liu T., Lytle M., Silenzio V., and Alm C. O. 2014. Toward macro-insights for suicide prevention: analyzing fine-grained distress at scale. In ACL 2014, Baltimore, MD, USA, pp. 107–117.
Homan C. M., Lu N., Tuurmcrochesteredu N. L. X., Lytle M. C., Lytle M., Rochester U., Silenzio V. M. B., and Silenzio V. 2014a. Social structure and depression in TrevorSpace. In Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work and Social Computing - CSCW ’14, Baltimore, MD, USA, pp. 615–624.
Homan C. M., Lu N., Tuurmcrochesteredu N. L. X., Lytle M. C., Lytle M., Rochester U., Silenzio V. M. B., and Silenzio V. 2014b. Social structure and depression in TrevorSpace. In Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work and Social Computing - CSCW ’14, Baltimore, MD, USA, pp. 615–624.
Horvitz E., and Mulligan D. 2015. Data, privacy, and the greater good. Science 349 (6245): 253255.
Hoyt T., and Pasupathi M. 2008. Blogging about trauma: linguistic measures of apparent recovery. E-Journal of Applied Psychology 4 (2): 5662.
Hussain M. S., Calvo R. A., Ellis L., Li J., Ospina-Pinillos L., Davenport T., and Hickie I., 2015. Nlg-based moderator response generator to support mental health. In Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems, Seoul, Republic of Korea, pp. 13851390.
Jones R., Pearson J., McGregor S., Cawsey A. J., Barrett A., Craig N., Atkinson J. M., Gilmour W. H., and McEwen J. 1999. Randomised trial of personalised computer based information for cancer patients. BMJ 319 (7219): 12411247.
Kaltenthaler E., Brazier J., De Nigris E., Tumur I., Ferriter M., Beverley C., Parry G., Rooney G., and Sutcliffe P. 2006. Computerised cognitive behaviour therapy for depression and anxiety update: a systematic review and economic evaluation. Health Technology Assessment 10 (33): 1186.
Kenny P., Parsons T. D., Gratch J., Leuski A., and Rizzo A. A. 2007. Virtual patients for clinical therapist skills training. In Intelligent Virtual Agents, Springer-Verlag Berlin Heidelberg, pp. 197210.
Kim S. M., Wang Y., Wan S., and Paris C., 2016. Data61-csiro systems at the clpsych 2016 shared task. In Proceedings of the 3rd Workshop on Computational Linguistics and Clinical Psychology, San Diego, CA, USA, pp. 128132.
Kotsiantis S. 2007. Supervised machine learning: a review of classification techniques. Informatica 31; 249268.
Kramer A. D., 2010. An unobtrusive behavioral model of gross national happiness. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Atlanta, GA, USA, pp. 287290.
Kramer A. D., Guillory J. E., and Hancock J. T. 2014. Experimental evidence of massive-scale emotional contagion through social networks. Proceedings of the National Academy of Sciences 111 (24): 87888790.
Larsen M. E., Boonstra T. W., Batterham P. J., O’Dea B., Paris C., and Christensen H. 2015. We feel: mapping emotion on twitter. IEEE Journal of Biomedical and Health Informatics 19 (4): 12461252.
Lawless N., and Lucas R. 2011. Predictors of regional well-being: a county level analysis. Social Indicators Research 101 (3): 341357.
Li A., Huang X., Hao B., O’Dea B., Christensen H., and Zhu T. 2015. Attitudes towards suicide attempts broadcast on social media: an exploratory study of chinese microblogs. PeerJ 3: e1209.
Liu M., Calvo R. A., Davenport T., and Hickie I. 2013. Moderator assistant: helping those who help via online mental health support groups. In Joint Workshop on Smart Health and Social Therapies, OzChi, pp. 1–4.
Luyckx K., Vaassen F., Peersman C., and Daelemans W. 2012. Fine-grained emotion detection in suicide notes: a thresholding approach to multi-label classification. Biomedical Informatics Insights 5 (Suppl 1): 6169.
Malmasi S., Zampieri M., and Dras M., 2016. Predicting post severity in mental health forums. In Proceedings of the 3rd Workshop on Computational Linguistics and Clinical Psychology, San Diego, CA, USA, pp. 133137.
Martínez-Miranda J., Bresó A., and García-Gómez J. M. 2012a. Modelling therapeutic empathy in a virtual agent to support the remote treatment of major depression. In ICAART (2), Vilamoura, Algarve, Portugal, pp. 264–269.
Martínez-Miranda J., Bresó A., and García-Gómez J. M., 2012b. The construction of a cognitive-emotional module for the Help4Mood’s virtual agent. In Proceedings of 1st Workshop on Information and Communication Technologies Applied to Mental Health, Valencia, Spain, pp. 3439.
Masuda N., Kurahashi I., and Onari H. 2013. Suicide ideation of individuals in online social networks. PloS One 8 (4): e62262.
McCart J. A., Finch D. K., Jarman J., Hickling E., Lind J. D., Richardson M. R., Berndt D. J., and Luther S. L. 2012. Using ensemble models to classify the sentiment expressed in suicide notes. Biomedical Informatics Insights 5 (Suppl 1): 7785.
Miller G., Beckwith R., Fellbaum C., Gross D., and Miller K. 1990. Introduction to wordnet: an on-line lexical database. Journal of Lexicography 3, 235244.
Milne D. N., Pink G., Hachey B., and Calvo R. A., 2016. Clpsych 2016 shared task: triaging content in online peer-support forums. In Proceedings of the 3rd Workshop on Computational Linguistics and Clinical Psychology, San Diego, CA, USA, pp. 118127.
Mitchell L., Frank M. R., Harris K. D., Dodds P. S., and Danforth C. M. 2013. The geography of happiness: connecting twitter sentiment and expression, demographics, and objective characteristics of place. PloS One 8 (5): e64417.
Moreno M., and Jelenchick L. 2011. Feeling bad on Facebook: depression disclosures by college students on a social networking site. Depression and Anxiety 28: 447455.
Myung S.-K., McDonnell D. D., Kazinets G., Seo H. G., and Moskowitz J. M. 2009. Effects of Web-and computer-based smoking cessation programs: meta-analysis of randomized controlled trials. Archives of Internal Medicine 169 (10): 929937.
Neve M., Morgan P. J., Jones P. R., and Collins C. E. 2010. Effectiveness of web-based interventions in achieving weight loss and weight loss maintenance in overweight and obese adults: a systematic review with meta-analysis. Obesity Reviews 11 (4): 306321.
Nguyen T., Phung D., Dao B., Venkatesh S., and Berk M. 2014. Affective and content analysis of online depression communities. IEEE Transactions on Affective Computing 5 (3): 217226.
Nigam K., McCallum A. K., Thrun S., and Mitchell T. 2000. Text classification from labeled and unlabeled documents using EM. Machine Learning 39 (2–3): 103134.
O’Dea B., and Campbell A. 2010. Healthy connections: online social networks and their potential for peer support. Studies in Health Technology and Informatics 168: 133140.
O’Dea B., Wan S., Batterham P. J., Calear A. L., Paris C., and Christensen H. 2015. Detecting suicidality on Twitter. Internet Interventions 2 (2): 183188.
Okun B., and Kantrowitz R., 2014. Effective Helping: Interviewing and Counseling Techniques. Cengage Learning. Toronto, Canda: Nelson Education.
Owen J. E., Klapow J. C., Roth D. L., Shuster J. L., Bellis J., Meredith R., and Tucker D. C. 2005. Randomized pilot of a self-guided internet coping group for women with early-stage breast cancer. Annals of Behavioral Medicine 30 (1): 5464.
Paul M., and Dredze M. 2011. You are what you tweet: Analyzing twitter for public health. In Proceedings of the 5th International AAAI Conference on Weblogs and Social Media, Barcelona, Spain, pp. 265–272.
Pedersen T., 2015. Screening twitter users for depression and ptsd with lexical decision lists. In Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, Denver, Colorado, pp. 4653.
Peek N., Combi C., Marin R., and Bellazzi R. 2015. Artificial intelligence in medicine thirty years of artificial intelligence in medicine (AIME) conferences: a review of research themes. Artificial Intelligence In Medicine 65 (1): 6173.
Pennebaker J., Kiecolt-Glaser J., and Glaser R. 1988. Disclosure of traumas and immune function: health implications for psychotherapy. Journal of Consulting and Clinical Psychology 56 (2): 239245.
Pennebaker J. W., 2011. The Secret Life of Pronouns: How Our Words Reflect Who We Are. New York, NY: Bloomsbury Press.
Pennebaker J. W., Boyd R. L., Jordan K., and Blackburn K. 2015. The development and psychometric properties of liwc2015. UT Faculty/Researcher Works.
Pennebaker J. W., and Chung C. K. 2007. Expressive writing, emotional upheavals, and health. Handbook of Health Psychology, Oxford University Press, USA, pp. 263284.
Pestian J., Nasrallah H., Matykiewicz P., Bennett A., and Leenaars A. 2010. Suicide note classification using natural language processing: a content analysis. Biomedical Informatics Insights 2010 (3): 1928.
Pestian J. P., Matykiewicz P., Linn-Gust M., South B., Uzuner O., Wiebe J., Cohen K. B., Hurdle J., and Brew C. 2012. Sentiment analysis of suicide notes: a shared task. Biomedical Informatics Insights 5 (Suppl 1): 316.
Pistrang N., Barker C., and Humphreys K. 2008. Mutual help groups for mental health problems: a review of effectiveness studies. American Journal of Community Psychology 42 (1–2): 110121.
Preoţiuc-Pietro D., Sap M., Schwartz H. A., and Ungar L., 2015. Mental illness detection at the world well-being project for the clpsych 2015 shared task. In Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, Denver, Colorado, pp. 4045.
Reiter E., and Dale R., 2000. Building Natural Language Generation Systems. Boston: MIT Press.
Reiter E., Robertson R., and Osman L. M. 2003. Lessons from a failure: generating tailored smoking cessation letters. Artificial Intelligence 144 (1): 4158.
Resnik P., Armstrong W., Claudino L., and Nguyen T., 2015. The university of maryland clpsych 2015 shared task system. In Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, Denver, Colorado, pp. 5460.
Resnik P., Resnik R., and Mitchell M. (eds.), 2014. Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality. Baltimore, Maryland, USA: Association for Computational Linguistics, pp. 1125.
Rincón-Nigro M., and Deng Z. 2013. A text-driven conversational avatar interface for instant messaging on mobile devices. IEEE Transactions on Human-Machine Systems 43 (3): 328332.
Ritterband L. M., Gonder-Frederick L. A., Cox D. J., Clifton A. D., West R. W., and Borowitz S. M. 2003. Internet interventions: in review, in use, and into the future. Professional Psychology: Research and Practice 34 (5): 527534.
Riva G., Calvo R. A., and Lisetti C. 2014. Cyberpsychology and affective computing. In Calvo R., D’Mello S., Gratch J., and Kappas A. (eds.), Handbook of Affective Computing, pp. 547558. New York: Oxford University Press.
Sadilek A., Homan C., Lasecki W., Silenzio V., and Kautz H., 2013. Modeling fine-grained dynamics of mood at scale. In WSDM, Rome, Italy, pp. 36.
Schwartz H. A., Eichstaedt J. C., Margaret L., Kern L., Dziurzynski M. A., Park G. J., Lakshmikanth S. K., Jha S., Seligman M. E. P., and Ungar L., 2013. Characterizing geographic variation in well-being using tweets. In Proceedings of the 7th International AAAI Conference on Weblogs and Social Media, Cambridge, Massachusetts, USA, pp. 583591.
Sebastiani F. 2002. Machine learning in automated text categorization. ACM Comput. Surv. 34 (1): 147.
Shneidman E., and Farberow N. (eds.), 1957. Clues to Suicide. New York: Harper and Row.
Signorini A., Segre A. M., and Polgreen P. M. 2011. The use of Twitter to track levels of disease activity and public concern in the US during the influenza A H1N1 pandemic. PloS One 6 (5): e19467.
Spasić I., Burnap P., Greenwood M., and Arribas-Ayllon M. 2012. A naïve Bayes Approach to classifying Topics in suicide notes. Biomedical Informatics Insights 5 (Suppl 1): 8797.
Spek V., Cuijpers P. I. M., Nyklícek I., Riper H., Keyzer J., and Pop V. 2007. Internet-based cognitive behaviour therapy for symptoms of depression and anxiety: a meta-analysis. Psychological Medicine 37 (03): 319328.
Strapparava C., and Mihalcea R., 2007. Semeval-2007 task 14: affective text. In Proceedings of the 4th International Workshop on Semantic Evaluations, Prague, Czech Republic, pp. 7074.
Strapparava C., and Mihalcea R., 2008. Learning to identify emotions in text. In Proceedings of the 2008 ACM Symposium on Applied Computing, Fortaleza, Ceara, Brazil, pp. 15561560.
Strapparava C., and Mihalcea R. 2014. Affect detection in texts. In Calvo R. A., D’Mello S., Gratch J., and Kappas A. (eds.), The Oxford Handbook of Affective Computing, Chapter 13, pp. 184203. New York: Oxford University Press.
Strapparava C., and Valitutti A., 2004. WordNet-Affect: an affective extension of WordNet. In LREC 2004-4th IInternational Conference on Language Resources and Evaluation, vol. 4, Lisbon, pp. 10831086.
Tausczik Y. R., and Pennebaker J. W. 2010. The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology 29 (1): 2454.
Weizenbaum J. 1966. ELIZA-a computer program for the study of natural language communication between man and machine. Communications of the ACM 9 (1): 3645.
Witten I. H., and Frank E., 2005. Data Mining: Practical Machine Learning Tools and Techniques. San Francisco, USA: Morgan Kaufmann.
Xu Y., Wang Y., Liu J., Tu Z., Sun J.-T., Tsujii J., and Chang E. 2012. Suicide note sentiment classification: a supervised approach augmented by web data. Biomedical Informatics Insights 5 (Suppl 1): 3141.
Yang H., Willis A., De Roeck A., and Nuseibeh B. 2012. A hybrid model for automatic emotion recognition in suicide notes. Biomedical Informatics Insights 5 (Suppl 1): 1730.
Yu N., Kübler S., Herring J., Hsu Y.-Y., Israel R., and Smiley C. 2012. LASSA: emotion detection via information fusion. Biomedical Informatics Insights 5 (Suppl 1): 7176.
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Natural Language Engineering
  • ISSN: 1351-3249
  • EISSN: 1469-8110
  • URL: /core/journals/natural-language-engineering
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×

Metrics

Altmetric attention score

Full text views

Total number of HTML views: 212
Total number of PDF views: 1004 *
Loading metrics...

Abstract views

Total abstract views: 1265 *
Loading metrics...

* Views captured on Cambridge Core between 30th January 2017 - 14th December 2017. This data will be updated every 24 hours.