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Communication Papers of the 17th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 32

The Mood of the Silver Economy: A Data Science Analysis of the Mood States of Older Adults and the Implications on their Wellbeing

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DOI: http://dx.doi.org/10.15439/2022F50

Citation: Communication Papers of the 17th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 32, pages 251258 ()

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Abstract. For the first time in the history of humanity, the number of people over 65 surpassed those under 5 in 2018. Undoubtedly, older people will play a significant role in the future of the economy and society in general, and technological innovation will be indispensable to support them. Thus, we were interested in learning how home automation could enable older people to live independently for longer. To better understand this, we held focus groups with UK senior citizens in 2021, and we analyzed the data derived from them from the perspective of affective computing. We have trained a machine learning classifier capable of distinguishing moods commonly associated with older adults. We have identified depression, sadness and anger as the most prominent mood states conveyed in our focus groups. Our practical insights can aid the design of strategic choices concerning the wellbeing of the ageing population.


  1. A. Ahtonen, “Healthy and active ageing: Turning the ’silver’ economy into gold,” Policy Brief, vol. 12, no. 3, p. 2012, 2012.
  2. Iberdrola, “Silver Economy,” 2022, https://www.iberdrola.com/innovation/silver-economy.
  3. F. Bran, M.-L. Popescu, and P. Stanciu, “Perspectives of silver economy in the European Union,” Revista de Management Comparat International, vol. 17, no. 2, p. 130, 2016.
  4. A. S. Zueva and T. S. Khrolenko, “Population ageing: Demographic security threat or silver industry development potential,” RUDN Journal of Public Administration, vol. 6, no. 3, pp. 234–242, 2019.
  5. A. Klimczuk, “Comparative analysis of national and regional models of the silver economy in the European Union,” A. Klimczuk, Comparative Analysis of National and Regional Models of the Silver Economy in the European Union,“International Journal of Ageing and Later Life, vol. 10, no. 2, pp. 31–59, 2016.
  6. J. Unützer and M. L. Bruce, “The elderly,” Mental Health Services Research, vol. 4, no. 4, pp. 245–247, 2002.
  7. C. Walker, L. C. Curry, and M. O. Hogstel, “Relocation stress syndrome in older adults transitioning from home to a long-term care facility: Myth or reality?” Journal of Psychosocial Nursing and Mental Health Services, vol. 45, no. 1, pp. 38–45, 2007.
  8. Interreg 2 Seas Mers Zeeën, “AGE IN,” 2022, https://www.interreg2seas.eu/en/AGEIN.
  9. A. Ollevier, G. Aguiar, M. Palomino, and I. S. Simpelaere, “How can technology support ageing in place in healthy older adults? A systematic review,” Public Health Reviews, vol. 41, no. 1, pp. 1–12, 2020.
  10. University of Plymouth, “Plymouth Ethics Online System (PEOS),” 2022, https://www.plymouth.ac.uk/research/plymouth-ethics-online-system.
  11. Council Housing and Housing Association, “The charter for social housing residents,” Ministry of Housing, Communities and Local Government, Tech. Rep., Nov. 2020, https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/936098/The_charter_for_social_housing_residents_-_social_housing_white_paper.pdf.
  12. C. K. Chung and J. W. Pennebaker, “Using computerized text analysis to assess threatening communications and behavior,” Threatening Communications and Behavior: Perspectives on the Pursuit of Public Figures, pp. 3–32, 2011.
  13. B. Liu, “Sentiment analysis and opinion mining,” Synthesis Lectures on Human Language Technologies, vol. 5, no. 1, pp. 1–167, 2012.
  14. P. Gonçalves, M. Araújo, F. Benevenuto, and M. Cha, “Comparing and combining sentiment analysis methods,” in Proceedings of the ACM Conference on Online Social Networks, 2013, pp. 27–38.
  15. M. A. Palomino, A. P. Varma, G. K. Bedala, and A. Connelly, “Investigating the Lack of Consensus Among Sentiment Analysis Tools,” in Human Language Technology. Challenges for Computer Science and Linguistics, Z. Vetulani, P. Paroubek, and M. Kubis, Eds. Cham: Springer International Publishing, 2020, pp. 58–72.
  16. R. A. Calvo and S. D’Mello, “Affect detection: An interdisciplinary review of models, methods, and their applications,” IEEE Transactions on Affective Computing, vol. 1, no. 1, pp. 18–37, 2010.
  17. J. Tao and T. Tan, “Affective computing: A review,” in International Conference on Affective Computing and Intelligent Interaction. Springer, 2005, pp. 981–995.
  18. S. S. Díaz et al., “Intelligent execution of behaviors in a NAO robot exposed to audiovisual stimulus,” in IEEE Colombian Conference on Robotics and Automation (CCRA). IEEE, 2018, pp. 1–6.
  19. R. W. Picard, Affective Computing. MIT press, 2000.
  20. M. Spruit, S. Verkleij, K. de Schepper, and F. Scheepers, “Exploring language markers of mental health in psychiatric stories,” Applied Sciences, vol. 12, no. 4, p. 2179, 2022.
  21. S. Chaffar and D. Inkpen, “Using a heterogeneous dataset for emotion analysis in text,” in Advances in Artificial Intelligence, C. Butz and P. Lingras, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011, pp. 62–67.
  22. N. Colnerič and J. Demšar, “Emotion recognition on Twitter: comparative study and training a unison model,” IEEE Transactions on Affective Computing, vol. 11, no. 3, pp. 433–446, 2018.
  23. P. Ekman, “An argument for basic emotions,” Cognition & emotion, vol. 6, no. 3-4, pp. 169–200, 1992.
  24. R. Plutchik, “A general psychoevolutionary theory of emotion,” in Theories of Emotion. Elsevier, 1980, pp. 3–33.
  25. J. C. Norcross, E. Guadagnoli, and J. O. Prochaska, “Factor structure of the profile of mood states (POMS): Two partial replications,” Journal of Clinical Psychology, vol. 40, no. 5, pp. 1270–1277, 1984.
  26. A. K. Pandey, R. Gelin, and A. Robot, “Pepper: The first machine of its kind,” IEEE Robotics & Automation Magazine, vol. 25, no. 3, pp. 40–48, 2018.
  27. Trint Ltd., “Trint: Audio Transcription Software,” 2022, https://trint.com/.
  28. J. S. Girgus, K. Yang, and C. V. Ferri, “The gender difference in depression: Are elderly women at greater risk for depression than elderly men?” Geriatrics, vol. 2, no. 4, 2017. [Online]. Available: https://www.mdpi.com/2308-3417/2/4/35
  29. C. Buckley, “Implementation of the SMART information retrieval system [technical report],” Cornell University, TR85-686, vol. 4, no. 4, p. 4, 1985.
  30. H. Schütze, C. D. Manning, and P. Raghavan, Introduction to Information Retrieval. Cambridge University Press Cambridge, 2008.
  31. C. Haynes et al., “Automatic classification of National Health Service feedback,” Mathematics, vol. 10, no. 6, p. 983, 2022.
  32. B. G. Berger and R. W. Motl, “Exercise and mood: A selective review and synthesis of research employing the profile of mood states,” Journal of Applied Sport Psychology, vol. 12, no. 1, pp. 69–92, 2000.
  33. D. M. McNair, M. Lorr, and L. F. Droppleman, Manual Profile of Mood States. Educational & Industrial Testing Service, 1971.
  34. A. Leunes and J. Burger, “Profile of mood states research in sport and exercise psychology: Past, present, and future,” Journal of Applied Sport Psychology, vol. 12, no. 1, pp. 5–15, 2000.
  35. S. Y. Cheung and E. T. Lam, “An innovative shortened bilingual version of the profile of mood states (POMS-SBV),” School Psychology International, vol. 26, no. 1, pp. 121–128, 2005.
  36. A. Pepe and J. Bollen, “Between conjecture and memento: Shaping a collective emotional perception of the future,” in AAAI Spring Symposium: Emotion, Personality, and Social Behavior, 2008, pp. 111–116.
  37. J. Bollen, H. Mao, and A. Pepe, “Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena,” in Proceedings of the AAAI Conference on Web and Social Media, vol. 5, no. 1, 2011, pp. 450–453.
  38. J. Morita, Y. Nagai, and T. Moritsu, “Relations between body motion and emotion: Analysis based on laban movement analysis,” in Proceedings of the Annual Meeting of the Cognitive Science Society, vol. 35, no. 35, 2013.
  39. M. Makita, A. Mas-Bleda, E. Stuart, and M. Thelwall, “Ageing, old age and older adults: A social media analysis of dominant topics and discourses,” Ageing & Society, vol. 41, no. 2, pp. 247–272, 2021.
  40. R. M. Kok and C. F. Reynolds, “Management of depression in older adults: a review,” Jama, vol. 317, no. 20, pp. 2114–2122, 2017.
  41. A. Fiske, J. L. Wetherell, and M. Gatz, “Depression in older adults,” Annual review of clinical psychology, vol. 5, pp. 363–389, 2009.
  42. R. Briggs, K. Tobin, R. A. Kenny, and S. P. Kennelly, “What is the prevalence of untreated depression and death ideation in older people? Data from the Irish longitudinal study on aging,” International psychogeriatrics, vol. 30, no. 9, pp. 1393–1401, 2018.
  43. B. E. Leonard, “Inflammation, depression and dementia: Are they connected?” Neurochemical research, vol. 32, no. 10, pp. 1749–1756, 2007.
  44. Mackenzie, B, “Scoring for POMS,” 2022, https://www.brianmac.co.uk/pomscoring.htm.
  45. W. S. Noble, “What is a support vector machine?” Nature biotechnology, vol. 24, no. 12, pp. 1565–1567, 2006.
  46. D. Berrar, “Bayes’ theorem and naive bayes classifier,” Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics, vol. 403, 2018.
  47. D. G. Kleinbaum, K. Dietz, M. Gail, M. Klein, and M. Klein, Logistic Regression. Springer, 2002.
  48. G. Biau, “Analysis of a random forests model,” The Journal of Machine Learning Research, vol. 13, no. 1, pp. 1063–1095, 2012.
  49. J. Nowak, A. Taspinar, and R. Scherer, “LSTM recurrent neural networks for short text and sentiment classification,” in International Conference on Artificial Intelligence and Soft Computing. Springer, 2017, pp. 553– 562.
  50. G. Research, “Google Colaboratory,” 2022, https://colab.research.google.com/.
  51. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
  52. X. Peng, “A comparative study of neural network for text classification,” in 2020 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS). IEEE, 2020, pp. 214–218.
  53. Python Software Foundation, “text2emotion 0.0.5,” 2022, https://pypi.org/project/text2emotion/.
  54. J. He and L. A. Freeman, “Are men more technology-oriented than women? the role of gender on the development of general computer self-efficacy of college students,” Journal of Information Systems Education, vol. 21, no. 2, pp. 203–212, 2010.
  55. T. L. Mitzner, J. B. Boron, C. B. Fausset, A. E. Adams, N. Charness, S. J. Czaja, K. Dijkstra, A. D. Fisk, W. A. Rogers, and J. Sharit, “Older adults talk technology: Technology usage and attitudes,” Computers in human behavior, vol. 26, no. 6, pp. 1710–1721, 2010.
  56. J. N. Beadle and C. E. De la Vega, “Impact of aging on empathy: Review of psychological and neural mechanisms,” Frontiers in psychiatry, vol. 10, p. 331, 2019.
  57. J. Buchler, Philosophical writings of Peirce. Dover, 1955.
  58. V. Pollard, “Ethics and reflective practice: Continuing the conversation,” Reflective Practice, vol. 9, no. 4, pp. 399–407, 2008.
  59. M. Sayag and G. Kavé, “The effects of social comparisons on subjective age and self-rated health,” Ageing & Society, pp. 1–14, 2021.
  60. S. Quine and S. Morrell, “Fear of loss of independence and nursing home admission in older Australians,” Health & Social Care in the Community, vol. 15, no. 3, pp. 212–220, 2007.
  61. B. D. James, P. A. Boyle, and D. A. Bennett, “Correlates of susceptibility to scams in older adults without dementia,” Journal of elder abuse & neglect, vol. 26, no. 2, pp. 107–122, 2014.
  62. V. Zarulli, J. A. B. Jones, A. Oksuzyan, R. Lindahl-Jacobsen, K. Christensen, and J. W. Vaupel, “Women live longer than men even during severe famines and epidemics,” Proceedings of the National Academy of Sciences, vol. 115, no. 4, pp. E832–E840, 2018.
  63. J. Lemaire, “Why do females live longer than males?” North American Actuarial Journal, vol. 6, no. 4, pp. 21–37, 2002.
  64. A. Steptoe, A. Shankar, P. Demakakos, and J. Wardle, “Social isolation, loneliness, and all-cause mortality in older men and women,” Proceedings of the National Academy of Sciences, vol. 110, no. 15, pp. 5797–5801, 2013.
  65. B. Cornwell, “Independence through social networks: Bridging potential among older women and men,” Journals of Gerontology Series B: Psychological Sciences and Social Sciences, vol. 66, no. 6, pp. 782–794, 2011.
  66. R. Mann, “Out of the shadows?: Grandfatherhood, age and masculinities,” Journal of Aging Studies, vol. 21, no. 4, pp. 281–291, 2007.
  67. F. Förster, A. Pabst, J. Stein, S. Röhr, M. Löbner, K. Heser, L. Miebach, A. Stark, A. Hajek, B. Wiese et al., “Are older men more vulnerable to depression than women after losing their spouse? evidence from three german old-age cohorts (agedifferent.de platform),” Journal of Affective Disorders, vol. 256, pp. 650–657, 2019.
  68. R. W. Kressig, S. L. Wolf, R. W. Sattin, M. O’Grady, A. Greenspan, A. Curns, and M. Kutner, “Associations of demographic, functional, and behavioral characteristics with activity-related fear of falling among older adults transitioning to frailty,” Journal of the American Geriatrics Society, vol. 49, no. 11, pp. 1456–1462, 2001.
  69. J. C. Goll, G. Charlesworth, K. Scior, and J. Stott, “Barriers to social participation among lonely older adults: The influence of social fears and identity,” PloS one, vol. 10, no. 2, 2015.
  70. T. Holtgraves, “Social psychology and language: Words, utterances, and conversations,” Handbook of Social Psychology, 2010.
  71. Y. R. Tausczik and J. W. Pennebaker, “The psychological meaning of words: LIWC and computerized text analysis methods,” Journal of language and social psychology, vol. 29, no. 1, pp. 24–54, 2010.
  72. S. Eggly, M. A. Manning, R. B. Slatcher, R. A. Berg, D. L. Wessel, C. J. Newth, T. P. Shanley, R. Harrison, H. Dalton, J. M. Dean et al., “Language analysis as a window to bereaved parents’ emotions during a parent—physician bereavement meeting,” Journal of Language and Social Psychology, vol. 34, no. 2, pp. 181–199, 2015.
  73. A. Storey, N. Coombs, and S. Leib, “Living longer: Caring in later working life,” UK Office for National Statistics, Tech. Rep., Mar. 2019.
  74. Department for Business, Energy & Industrial Strategy, “The grand challenge missions,” Industrial Strategy, Tech. Rep., Jan. 2021.