A happiness degree predictor using the conceptual data structure for deep learning architectures
Open accessAbstract
Background and objective Happiness is a universal fundamental human goal. Since the emergence of Positive Psychology, a major focus in psychological research has been to study the role of certain factors in the prediction of happiness. The conventional methodologies are based on linear relationships, such as the commonly used Multivariate Linear Regression (MLR), which may suffer from the lack of representative capacity to the varied psychological features. Using Deep Neural Networks (DNN), we define a Happiness Degree Predictor (H-DP) based on the answers to five psychometric standardized questionnaires.Methods A Data-Structure driven architecture for DNNs (D-SDNN) is proposed for defining a HDP in which the network architecture enables the conceptual interpretation of psychological factors associated to happiness. Four different neural network configurations have been tested, varying the number of neurons and the presence or absence of bias in the hidden layers. Two metrics for evaluating the influence of conceptual dimensions have been defined and computed: one quantifies the influence weight of the conceptual dimension in absolute terms and the other one pinpoints the direction (positive or negative) of the influence.Materials A cross-sectional survey targeting non-institutionalized adult population residing in Spain was completed by 823 cases. The total of 111 elements of the survey are grouped by socio-demographic data and by five psychometric scales (Brief COPE Inventory, EPQR-A, GHQ-28, MOS-SSS and SDHS) measuring several psychological factors acting one as the outcome (SDHS) and the four others as predictors.Results Our D-SDNN approach provided a better outcome (MSE: 1.46·10-2) than MLR (MSE: 2.30·10-2), hence improving by 37% the predictive accuracy, and allowing to simulate the conceptual structure.Conclusions We observe a better performance of Deep Neural Networks (DNN) with respect to traditional methodologies. This demonstrates its capability to capture the conceptual structure for predicting happiness degree through psychological variables assessed by standardized questionnaires. It also permits to estimate the influence of each factor on the outcome without assuming a linear relationship.
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Happiness is not only the disappearance of negative emotions and emotional states, but also the existence of positive emotions and emotional states.
“This means that happiness is not only the disappearance of negative emotions and emotional states, but also the existence of positive emotions and emotional states (10).”
From Effects of Short Video Addiction on the Motivation and Well-Being of Chinese Vocational College Students (2022) · cites this paper as [10]
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Cites
Convolutional Neural Networks for Sentence Classification
Kim, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1181 · Cited by 14,831 · see citing works
Positive psychology: An introduction.
Seligman, M. E. P., & Csikszentmihalyi, M. (2000) · American Psychologist · doi:10.1037/0003-066x.55.1.5 · Cited by 11,351 · see citing works
Well-being : the foundations of hedonic psychology
Kahneman, D. J., Diener, E. F., & Schwarz, N. (1999) · Cited by 7,472 · see citing works
You want to measure coping but your protocol’ too long: Consider the brief cope
Carver, C. S. (1997) · International Journal of Behavioral Medicine · doi:10.1207/s15327558ijbm0401_6 · Cited by 7,361 · see citing works
The benefits of frequent positive affect: Does happiness lead to success?
Lyubomirsky, S., King, L., & Diener, E. (2005) · Psychological Bulletin · doi:10.1037/0033-2909.131.6.803 · Cited by 7,143 · see citing works
Subjective well-being. The science of happiness and a proposal for a national index
Diener, E. (2000) · American Psychologist · doi:10.1037/0003-066x.55.1.34 · Cited by 7,121 · see citing works
Sherbourne, C. D., & Stewart, A. L. (1991) · Social Science & Medicine · doi:10.1016/0277-9536(91)90150-b · Cited by 6,643 · see citing works
Statistics notes: Cronbach's alpha: Table 1
Bland, J. M., & Altman, D. G. (1997) · BMJ · doi:10.1136/bmj.314.7080.572 · Cited by 5,300 · see citing works
Neural Networks and the Bias/Variance Dilemma
Geman, S., Bienenstock, E., & Doursat, R. (1992) · Neural Computation · doi:10.1162/neco.1992.4.1.1 · Cited by 3,578 · see citing works
Sin, N. L., & Lyubomirsky, S. (2009) · Journal of Clinical Psychology · doi:10.1002/jclp.20593 · Cited by 2,955 · see citing works
Symbolic Boolean manipulation with ordered binary-decision diagrams
Bryant, R. E. (1992) · ACM Computing Surveys · doi:10.1145/136035.136043 · Cited by 2,032 · see citing works
Ensembling neural networks: Many could be better than all
Zhou, Z.-H., Wu, J., & Tang, W. (2002) · Artificial Intelligence · doi:10.1016/s0004-3702(02)00190-x · Cited by 1,964 · see citing works
Tu, J. V. (1996) · Journal of Clinical Epidemiology · doi:10.1016/s0895-4356(96)00002-9 · Cited by 1,818 · see citing works
Affective Computing and Sentiment Analysis
Cambria, E. (2016) · IEEE Intelligent Systems · doi:10.1109/mis.2016.31 · Cited by 1,445 · see citing works
The funds, friends, and faith of happy people.
Myers, D. G. (2000) · American Psychologist · doi:10.1037/0003-066x.55.1.56 · Cited by 1,186 · see citing works
Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts
dos Santos, C., & de Bayser, M. G. (2014) · International Conference on Computational Linguistics · Cited by 1,162 · see citing works
Resolution Adopted By The General Assembly
(1984) · International Legal Materials · doi:10.1017/s002078290003076x · Cited by 1,150 · see citing works
The funds, friends, and faith of happy people.
Myers, D. G. (2000) · American Psychologist · doi:10.1037//0003-066x.55.1.56 · Cited by 1,130 · see citing works
Convolutional Neural Networks for Sentence ClassificationPreprint
Kim, Y. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1408.5882 · Cited by 1,112 · see citing works
Valdez, P., & Mehrabian, A. (1994) · Journal of Experimental Psychology: General · doi:10.1037/0096-3445.123.4.394 · Cited by 1,104 · see citing works
Quality of life: An approach integrating opportunities, human needs, and subjective well-being
Costanza, R., Fisher, B., Ali, S. H., Beer, C. C., Bond, L. A., Boumans, R. M. J., Danigelis, N. L., Dickinson, J., Elliott, C. M., Farley, J., et al. (2006) · Ecological Economics · doi:10.1016/j.ecolecon.2006.02.023 · Cited by 1,043 · see citing works
Speech emotion recognition using deep neural network and extreme learning machine
Han, K., Yu, D., & Tashev, I. (2014) · Interspeech (USB) · doi:10.21437/interspeech.2014-57 · Cited by 824 · see citing works
A Catastrophe Model for Developing Service Satisfaction Strategies
Oliva, T. A., Oliver, R. L., & MacMillan, I. C. (1992) · Journal of Marketing · doi:10.1177/002224299205600306 · Cited by 816 · see citing works
The Relationship Between Social Support and Subjective Well-Being Across Age
Siedlecki, K. L., Salthouse, T. A., Oishi, S., & Jeswani, S. (2013) · Social Indicators Research · doi:10.1007/s11205-013-0361-4 · Cited by 810 · see citing works
Causes and correlates of happiness.
Argyle, M. (1999) · Cited by 742 · see citing works
Proceedings of the 22nd ACM international conference on Multimedia
(2014) · doi:10.1145/2647868 · Cited by 741 · see citing works
The Effects of Color on Emotions
Kane, B. A., Campbell, J. E., & Katz, G. S. (2013) · PsycEXTRA Dataset · doi:10.1037/e314722004-001 · Cited by 731 · see citing works
If we are so rich, why aren't we happy?
Csíkszentmihályi, M. (1999) · American Psychologist · doi:10.1037/0003-066x.54.10.821 · Cited by 727 · see citing works
Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks
You, Q., Luo, J., Jin, H., & Yang, J. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9179 · Cited by 571 · see citing works
Poria, S., Cambria, E., & Gelbukh, A. F. (2015) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d15-1303 · Cited by 553 · see citing works
Francis, L. J., Brown, L. B., & Philipchalk, R. P. (1992) · Personality and Individual Differences · doi:10.1016/0191-8869(92)90073-x · Cited by 550 · see citing works
Seligman, M. E. P. (2008) · Applied Psychology · doi:10.1111/j.1464-0597.2008.00351.x · Cited by 502 · see citing works
Recognition of emotions using multimodal physiological signals and an ensemble deep learning model
Yin, Z., Zhao, M., Wang, Y., Yang, J., & Zhang, J. (2017) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2016.12.005 · Cited by 416 · see citing works
How do children cope with global climate change? Coping strategies, engagement, and well-being
Ojala, M. (2012) · Journal of Environmental Psychology · doi:10.1016/j.jenvp.2012.02.004 · Cited by 400 · see citing works
Schueller, S. M., & Seligman, M. E. P. (2010) · The Journal of Positive Psychology · doi:10.1080/17439761003794130 · Cited by 372 · see citing works
Neural network architectures and learning algorithms
Wilamowski, B. M. (2009) · IEEE Industrial Electronics Magazine · doi:10.1109/mie.2009.934790 · Cited by 355 · see citing works
Rapid assessment of well‐being: The Short Depression‐Happiness Scale (SDHS)
Joseph, S., Linley, P. A., Harwood, J., Lewis, C. A., & McCollam, P. (2004) · Psychology and Psychotherapy Theory Research and Practice · doi:10.1348/1476083042555406 · Cited by 270 · see citing works
DeepSentiBank: Visual Sentiment Concept Classification with Deep Convolutional Neural NetworksPreprint
Chen, T., Borth, D., Darrell, T., & Chang, S. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1410.8586 · Cited by 263 · see citing works
Lessons in neural network training: overfitting may be harder than expected
Lawrence, S., Giles, C. L., & Tsoi, A. C. (1997) · Cited by 234 · see citing works
Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep NetworksPreprint
You, Q., Luo, J., Jin, H., et al. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1509.06041 · Cited by 209 · see citing works
EEG-based mild depressive detection using feature selection methods and classifiers
Li, X., Hu, B., Sun, S., & Cai, H. (2016) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2016.08.010 · Cited by 189 · see citing works
Chan, Y. K., & Lee, R. P. L. (2006) · Journal of Happiness Studies · doi:10.1007/s10902-005-1915-1 · Cited by 187 · see citing works
The effect of perceived social support on subjective well-being
Gülaçtı, F. (2010) · Procedia - Social and Behavioral Sciences · doi:10.1016/j.sbspro.2010.03.602 · Cited by 186 · see citing works
User-level psychological stress detection from social media using deep neural network
Lin, H., Jia, J., Guo, Q., Xue, Y., Li, Q., Jie, H. J., Cai, L., & Feng, L. (2014) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2647868.2654945 · Cited by 183 · see citing works
Eerola, T., Lartillot, O., & Toiviainen, P. (2009) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.1416729 · Cited by 170 · see citing works
Furnham, A. F., & Brewin, C. R. (1990) · Personality and Individual Differences · doi:10.1016/0191-8869(90)90138-h · Cited by 157 · see citing works
Campos, D., Cebolla, A., Quero, S., Bretón-López, J. M., Botella, C., Soler, J., García‐Campayo, J., Demarzo, M., & Baños, R. (2015) · Personality and Individual Differences · doi:10.1016/j.paid.2015.08.040 · Cited by 145 · see citing works
Personality and happiness: Is mental health a mediator?
Liu, L., & Shih, J. B. (1997) · Personality and Individual Differences · doi:10.1016/s0191-8869(96)00187-0 · Cited by 134 · see citing works
Goldin, P. R., Hutcherson, C. A. C., Ochsner, K. N., Glover, G. H., Gabrieli, J. D. E., & Gross, J. J. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2005.03.018 · Cited by 133 · see citing works
Relationships between happiness and personality
Brebner, J., Donaldson, J., Kirby, N. H., & Ward, L. (1995) · Personality and Individual Differences · doi:10.1016/0191-8869(95)00022-x · Cited by 119 · see citing works
Learning emotion-based acoustic features with deep belief networks
Schmidt, E. M., & Kim, Y. E. (2011) · IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) · doi:10.1109/aspaa.2011.6082328 · Cited by 89 · see citing works
Rantanen, M., Mauno, S., Kinnunen, U. M., & Rantanen, J. (2011) · International Journal of Stress Management · doi:10.1037/a0022007 · Cited by 73 · see citing works
van Campen, C., & Iedema, J. (2007) · Quality of Life Research · doi:10.1007/s11136-006-9147-3 · Cited by 72 · see citing works
Zhang, J., Miao, D., Sun, Y., Xiao, R., Ren, L., Xiao, W., & Peng, J. (2013) · Social Indicators Research · doi:10.1007/s11205-013-0520-7 · Cited by 71 · see citing works
Personal and environmental correlates of happiness
Liu, L., Shih, J. B., Lin, Y. Y., & Ju, L. S. (1997) · Personality and Individual Differences · doi:10.1016/s0191-8869(97)80011-6 · Cited by 64 · see citing works
International Society for Music Information Retrieval
van Kranenburg, P. (2014) · Data Archiving and Networked Services (DANS) · Cited by 56 · see citing works
Pishva, N., Ghalehban, M., Moradi, A., & Hoseini, L. (2011) · Procedia - Social and Behavioral Sciences · doi:10.1016/j.sbspro.2011.10.084 · Cited by 45 · see citing works
New avenues in knowledge bases for natural language processing
Cambria, E., Schuller, B. W., Xia, Y., & White, B. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2016.07.025 · Cited by 44 · see citing works
Computational Intelligence for Big Social Data Analysis [Guest Editorial]
Cambria, E., Howard, N., Xia, Y., & Chua, T. (2016) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2016.2572481 · Cited by 40 · see citing works
Jin (1998) · doi:10.1002/(sici)1097-4679(199806)54:4
Flagged first, then by citations
3 of the 63 references couldn’t be matched to a known paper.
Research works printed but not linked: 7
- United Nations Foundation, Pharrel Williams tells the world: A Happy Planet = Happy People (2015). URL http://www.unfoundation.org/news-and-media/press-releases/2015/
- S. Joseph, C. A. Lewis, The depression-happiness scale: Reliability and validity of a bipolar self-report scale, Journal of Clinical Psychology 54 (4) (1998) 537-544.
- J. M. Bland, D. G. Altman, Statistics notes: Cronbach's alpha, British Medical Journal 314 (7080) (1997) 572.
- V. Bonilla, Confiabilidad, en el boletín informativo INEVA en acción, Vol. 2.
- I. H. Bernstein, J. Nunnally, Psychometric theory, New York: McGraw-Hill.
- S. Garavaglia, A. Sharma, A smart guide to dummy variables: Four appli- cations and a macro, in: Proceedings of the Northeast SAS Users Group Conference, 1998, p. 43.
- R. M. Warner, D. Rasco, Structural equation models for prediction of sub- jective well-being: Modeling negative affect as a separate outcome, The Journal of Happiness & Well-Being 2 (1) (2014) 34-50.
Cited by
Sorted by citations
Ye, J.-H., Wu, Y.-T., Wu, Y.-F., Chen, M.-Y., & Ye, J.-N. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.847672 · Cited by 221 · see citing works
“This means that happiness is not only the disappearance of negative emotions and emotional states, but also the existence of positive emotions and emotional states (10).”
BackgroundSpeech Emotion Recognition UsingConvolutional Neural Network and Long-Short TermMemory
Dangol, R., Alsadoon, A., Prasad, P. W. C., Seher, I., & Alsadoon, O. H. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09693-w · Cited by 61 · see citing works
Economic Stimulus Through Bank Regulation: Government Responses to the COVID-19 Crisis
Polyzos, E., Samitas, A., & Kampouris, I. (2021) · Journal of International Financial Markets Institutions and Money · doi:10.1016/j.intfin.2021.101444 · Cited by 36 · see citing works
Quantitatively Interpreting Residents Happiness Prediction by Considering Factor–Factor Interactions
Li, L., Wu, X., Kong, M., Liu, J., & Zhang, J. (2023) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2023.3246181 · Cited by 35 · see citing works
Wu, X., Li, L., Tao, X., Yuan, J., & Xie, H. (2025) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2025.3537918 · Cited by 33 · see citing works
Who is unhappy for Brexit? A machine-learning, agent-based study on financial instability
Polyzos, E., Samitas, A., & Katsaiti, M. (2020) · International Review of Financial Analysis · doi:10.1016/j.irfa.2020.101590 · Cited by 19 · see citing works
Towards the Quantitative Interpretability Analysis of Citizens Happiness Prediction
Li, L., Wu, X., Kong, M., Zhou, D. M., & Tao, X. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/707 · Cited by 11 · see citing works
Pérez‐Benito, F. J., Conejero, J. A., Sáez, C., Garcia-Gomez, J. M., Navarro‐Pardo, E., Florêncio, L. L., & Fernández‐de‐las‐Peñas, C. (2019) · Pain Practice · doi:10.1111/papr.12854 · Cited by 11 · see citing works
Prediction analysis of the happiness ranking of countries based on macro level factors
Handayani, D. O. D., Lubis, M., & Lubis, A. R. (2022) · IAES International Journal of Artificial Intelligence · doi:10.11591/ijai.v11.i2.pp666-678 · Cited by 8 · see citing works
Li, M., Lian, S., Wang, F., Zhou, Y., Chen, B., Guan, L., & Yan, W. (2019) · Scientific Reports · doi:10.1038/s41598-019-53206-1 · Cited by 6 · see citing works
Network Learning Approaches to study World HappinessPreprint
Dixit, S., Chaudhary, M., & Sahni, N. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.09181 · Cited by 6 · see citing works
Chen, S., Yang, M., & Lin, Y. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.1012796 · Cited by 5 · see citing works
Application of machine learning to cluster countries based on dominant parameters of happiness index
Athavale, V. A., Arora, S., & Yadav, R. S. (2021) · International Journal of Education and Science · doi:10.26697/ijes.2021.3.1 · Cited by 5 · see citing works
Pérez‐Benito, F. J., Garcia-Gomez, J. M., Navarro‐Pardo, E., & Conejero, J. A. (2020) · Mathematical Methods in the Applied Sciences · doi:10.1002/mma.6567 · Cited by 5 · see citing works
Wijaya, B. N., Park, Y., Jeung, J. H., & Lee, K. (2025) · Environmental Impact Assessment Review · doi:10.1016/j.eiar.2025.108135 · Cited by 2 · see citing works
Spirituality and Happiness: A Neuroscientific Perspective
Arora, R., & Mandal, M. K. (2023) · Springer eBooks · doi:10.1007/978-981-99-3493-5_2 · Cited by 2 · see citing works
Aligning Bytes with Bliss: Integrating Happiness Computing with Sociological Insight
Wu, X., Li, L., Tao, X., & Li, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0811-9_13 · Cited by 1 · see citing works
del Azuay, U., Lima-Castro, S., Bueno-Pacheco, A., del Azuay, U., Árias-Medina, P., del Azuay, U., Aguilar-Sizer, M., del Azuay, U., Cabrera-Vélez, M., del Azuay, U., et al. (2021) · doi:10.33324/ceuazuay.168
Li, Y., Wang, D., & Sun, X. (2023) · International Conference on Education, Knowledge and Information Management (ICEKIM) · doi:10.4108/eai.26-5-2023.2337310
Sohn, J.-R., Lee, G., Kim, J., Ahn, C. R., & Lee, S. H. (2025) · Journal of Management in Engineering · doi:10.1061/jmenea.meeng-6699
- Authors
- Pérez-Benito, F. J., Villacampa-Fernández, P., Conejero, J. A., García-Gómez, J. M., & Navarro-Pardo, E.
- Affiliations
- Universitat de València (ES)
- Universitat Politècnica de València (ES)
- Venue
- Computer Methods and Programs in Biomedicine
- Publisher
- Elsevier BV
- Published
- 2019-01
- Volume
- 168
- Pages
- 59-68
- ISSN
- 0169-2607 (ISSN-L)
- OpenAlex
- W2768732579
- Cited by
- 20
- References
- 63
- Citations by year
- 2025: 3 · 2024: 1 · 2023: 3 · 2022: 4 · 2021: 3 · 2020: 4 · 2019: 2
- FWCI
- 2.35
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Computer Methods and Programs in Biomedicine (publishedVersion)
- PubMed (publishedVersion)
- RiuNet (Universitat Politècnica de València) (submittedVersion, cc-by-nc-nd)
- Topics
- Social Sciences › Psychology › Social Psychology › Psychological Well-being and Life Satisfaction
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- happiness prediction, subjective well-being, positive psychology, deep neural network, psychometric questionnaire, psychological factors, Brief COPE, GHQ-28, nonlinear relationship
- MeSH terms
- Deep Learning, Adaptation, Psychological, Algorithms, Cross-Sectional Studies, Emotions, Female, Happiness, Humans, Male, Medical Informatics, Models, Psychological, Predictive Value of Tests, Psychometrics, Surveys and Questionnaires, Social Support, Software, Stress, Psychological, Multivariate Analysis
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