Investigating the impact of emotion on temporal orientation in a deep multitask setting.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
11 01 2022
11 01 2022
Historique:
received:
04
05
2020
accepted:
09
11
2021
entrez:
12
1
2022
pubmed:
13
1
2022
medline:
13
1
2022
Statut:
epublish
Résumé
Temporal orientation is an important aspect of human cognition which shows how an individual emphasizes past, present, and future. Theoretical research in psychology shows that one's emotional state can influence his/her temporal orientation. We hypothesize that measuring human temporal orientation can benefit from concurrent learning of emotion. To test this hypothesis, we propose a deep learning-based multi-task framework where we concurrently learn a unified model for temporal orientation (our primary task) and emotion analysis (secondary task) using tweets. Our multi-task framework takes users' tweets as input and produces three temporal orientation labels (past, present or future) and four emotion labels (joy, sadness, anger, or fear) with intensity values as outputs. The classified tweets are then grouped for each user to obtain the user-level temporal orientation and emotion. Finally, we investigate the associations between the users' temporal orientation and their emotional state. Our analysis reveals that joy and anger are correlated to future orientation while sadness and fear are correlated to the past orientation.
Identifiants
pubmed: 35017584
doi: 10.1038/s41598-021-04331-3
pii: 10.1038/s41598-021-04331-3
pmc: PMC8752665
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
493Subventions
Organisme : Horizon 2020 project STOP Obesity Platform
ID : 823978
Informations de copyright
© 2022. The Author(s).
Références
Marquardt, J. et al. Age and gender identification in social media. in Working Notes for CLEF 2014 Conference, Sheffield, UK, September 15–18, 2014. 1129–1136 (2014).
Sap, M. et al. Developing age and gender predictive lexica over social media. in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP. 1146–1151 (2014).
Dodds, P. S., Harris, K. D., Kloumann, I. M., Bliss, C. A. & Danforth, C. M. Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PloS one 6, 1–26 (2011).
doi: 10.1371/journal.pone.0026752
Choudhury, M. D., Counts, S. & Horvitz, E. Predicting postpartum changes in emotion and behavior via social media. in 2013 ACM SIGCHI Conference on Human Factors in Computing Systems. 3267–3276 (2013).
Kosinski, M., Stillwell, D. & Graepel, T. Private traits and attributes are predictable from digital records of human behavior. Proc. Natl. Acad. Sci. 110, 5802–5805 (2013).
pubmed: 23479631
pmcid: 3625324
doi: 10.1073/pnas.1218772110
Zimbardo, P. G. & Boyd, J. N. Putting time in perspective: A valid, reliable individual-differences metric. in Time Perspective Theory; Review, Research and Application. 17–55 (Springer, 2015).
Zimbardo, P. G. & Boyd, J. N. Putting time in perspective: A valid, reliable individual-differences metric. J. Pers. Soc. Psychol. 77, 1271–1288 (1999).
doi: 10.1037/0022-3514.77.6.1271
Zimbardo, P. & Boyd, J. The Time Paradox: The New Psychology of Time that Will Change Your Life (Simon and Schuster, 2008).
Keough, K. A., Zimbardo, P. G. & Boyd, J. N. Who’s smoking, drinking, and using drugs? Time perspective as a predictor of substance use. Basic Appl. Soc. Psychol. 21, 149–164 (1999).
doi: 10.1207/S15324834BA210207
Lasane, T. P. & Jones, J. M. Temporal orientation and academic goal-setting: The mediating properties of a motivational self. J. Soc. Behav. Pers. 14, 31–31 (1999).
Boyd, J. N. & Zimbardo, P. G. Time Perspective, Health, and Risk Taking. in Understanding Behavior in the Context of Time. 85–107 (2005).
Boniwell, I., Osin, E., Alex Linley, P. & Ivanchenko, G. V. A question of balance: Time perspective and well-being in British and Russian samples. J. Positive Psychol. 5, 24–40 (2010).
Stolarski, M., Matthews, G., Postek, S., Zimbardo, P. G. & Bitner, J. How we feel is a matter of time: Relationships between time perspectives and mood. J. Happiness Stud. 15, 809–827 (2014).
doi: 10.1007/s10902-013-9450-y
Webley, P. & Nyhus, E. K. Parents’ influence on children’s future orientation and saving. J. Econ. Psychol. 27, 140–164 (2006).
doi: 10.1016/j.joep.2005.06.016
Drake, L., Duncan, E., Sutherland, F., Abernethy, C. & Henry, C. Time perspective and correlates of wellbeing. Time Soc. 17, 47–61 (2008).
doi: 10.1177/0961463X07086304
Adams, J. & Nettle, D. Time perspective, personality and smoking, body mass, and physical activity: An empirical study. Br. J. Health Psychol. 14, 83–105 (2009).
pubmed: 18435866
doi: 10.1348/135910708X299664
Kammann, R. & Flett, R. Affectometer 2: A scale to measure current level of general happiness. Aust. J. Psychol. 35, 259–265 (1983).
doi: 10.1080/00049538308255070
Desmyter, F. & De Raedt, R. The relationship between time perspective and subjective well-being of older adults. Psychol. Belg. 52, 19–38 (2012).
doi: 10.5334/pb-52-1-19
Park, G. et al. Living in the past, present, and future: Measuring temporal orientation with language. J. Pers. 85, 270–280 (2017).
pubmed: 26710321
doi: 10.1111/jopy.12239
Hasanuzzaman, M., Kamila, S., Kaur, M., Saha, S. & Ekbal, A. Temporal orientation of tweets for predicting income of users. in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Vol. 2: Short Papers). Vol. 2. 659–665 (2017).
Kamila, S., Hasanuzzaman, M., Ekbal, A., Bhattacharyya, P. & Way, A. Fine-grained temporal orientation and its relationship with psycho-demographic correlates. in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Vol. 1 (Long Papers). 663–674 (2018).
Mikal, J., Hurst, S. & Conway, M. Ethical issues in using twitter for population-level depression monitoring: A qualitative study. BMC Med. Ethics 17, 1–11 (2016).
doi: 10.1186/s12910-016-0105-5
Townsend, L. & Wallace, C. Social media research: A guide to ethics. Univ. Aberdeen 1, 16 (2016).
McCormick, T. H., Lee, H., Cesare, N., Shojaie, A. & Spiro, E. S. Using twitter for demographic and social science research: Tools for data collection and processing. Soc. Methods Res. 46, 390–421 (2017).
doi: 10.1177/0049124115605339
Ahmed, W., Bath, P. A. & Demartini, G. Using twitter as a data source: An overview of ethical, legal, and methodological challenges. Ethics Online Res. (2017).
Schwartz, H. A. et al. Extracting human temporal orientation in Facebook language. in Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics-Human Language Technologies (NAACL). 409–419 (2015).
Kamila, S., Hasanuzzaman, M., Ekbal, A. & Bhattacharyya, P. Resolution of grammatical tense into actual time, and its application in time perspective study in the tweet space. PloS one 14, 1–20 (2019).
doi: 10.1371/journal.pone.0211872
Carstensen, L. L. Motivation for social contact across the life span: A theory of socioemotional selectivity. Nebraska Sympos. Motiv. 40, 209–254 (1993).
Lang, F. R. & Carstensen, L. L. Time counts: Future time perspective, goals, and social relationships. Psychol. Aging 17, 125–139 (2002).
pubmed: 11931281
doi: 10.1037/0882-7974.17.1.125
Hornik, J. The role of affect in consumers’ temporal judgments. Psychol. Market. 10, 239–255 (1993).
doi: 10.1002/mar.4220100306
Hornik, J. Time estimation and orientation mediated by transient mood. J. Socio-Econ. 21, 209–227 (1992).
doi: 10.1016/1053-5357(92)90010-5
Karniol, R. & Ross, M. The motivational impact of temporal focus: Thinking about the future and the past. Annu. Rev. Psychol. 47, 593–620 (1996).
pubmed: 15012489
doi: 10.1146/annurev.psych.47.1.593
Mohammad, S. M., Bravo-Marquez, F., Salameh, M. & Kiritchenko, S. Semeval-2018 task 1: Affect in tweets. in Proceedings of International Workshop on Semantic Evaluation (SemEval-2018) (2018).
Preoţiuc-Pietro, D., Lampos, V. & Aletras, N. An analysis of the user occupational class through twitter content. in Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Vol. 1: Long Papers). 1754–1764 (2015).
Wallace, M. & Rabin, A. I. Temporal experience. Psychol. Bull. 57, 213–236 (1960).
pubmed: 13842596
doi: 10.1037/h0041410
Hoornaert, J. Time perspective theoretical and methodological considerations. Psychol. Belg. 13, 265–294 (1973).
Koriat, A., Fischhoff, B. & Razel, O. An inquiry into the process of temporal orientation. Acta Psychol. 40, 57–73 (1976).
doi: 10.1016/0001-6918(76)90044-5
Lennings, C. J. & Burns, A. M. Time perspective: Temporal extension, time estimation, and impulsivity. J. Psychol. 132, 367–380 (1998).
pubmed: 9637020
doi: 10.1080/00223989809599271
Jones, J. M. Cultural differences in temporal perspectives: Instrumental and expressive behaviors in time. Soc. Psychol. Time New Perspect. 91, 21–38 (1988).
Strathman, A., Gleicher, F., Boninger, D. S. & Edwards, C. S. The consideration of future consequences: Weighing immediate and distant outcomes of behavior. J. Pers. Soc. Psychol. 66, 742–752 (1994).
doi: 10.1037/0022-3514.66.4.742
Holman, E. A. & Silver, R. C. Getting stuck in the past: Temporal orientation and coping with trauma. J. Pers. Soc. Psychol. 74, 1146–1163 (1998).
pubmed: 9599436
doi: 10.1037/0022-3514.74.5.1146
Agnew, C. R. & Loving, T. J. Future time orientation and condom use attitudes, intentions, and behavior. J. Soc. Behav. Pers. 13, 755–764 (1998).
Das, T. K. Strategic planning and individual temporal orientation. Strateg. Manag. J. 8, 203–209 (1987).
doi: 10.1002/smj.4250080211
Brown, C. M. & Segal, R. Ethnic differences in temporal orientation and its implications for hypertension management. J. Health Soc. Behav. 37, 350–361 (1996).
pubmed: 8997890
doi: 10.2307/2137262
Greaves, G. Temporal orientation in suicidal patients. Percept. Motor Skills 33, 1020–1020 (1971).
pubmed: 5127182
doi: 10.2466/pms.1971.33.3.1020
Kamila, S., Hasanuzzaman, M., Ekbal, A. & Bhattacharyya, P. Measuring temporal distance focus from tweets and investigating its association with psycho-demographic attributes. IEEE Trans. Affect. Comput. (2020).
Agrawal, A. & An, A. Unsupervised emotion detection from text using semantic and syntactic relations. In Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology. Vol. 1. 346–353 (IEEE Computer Society, 2012).
Calvo, R. A. & Mac Kim, S. Emotions in text dimensional and categorical models. Comput. Intell. 29, 527–543 (2013).
doi: 10.1111/j.1467-8640.2012.00456.x
Mohammad, S., Bravo-Marquez, F., Salameh, M. & Kiritchenko, S. Semeval-2018 task 1: Affect in tweets. in Proceedings of the 12th International Workshop on Semantic Evaluation. 1–17 (2018).
Mohammad, S. & Bravo-Marquez, F. WASSA-2017 shared task on emotion intensity. in Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. 34–49 (Association for Computational Linguistics, 2017). https://www.aclweb.org/anthology/W17-5205 .
Mohammad, S. M. & Turney, P. D. Crowdsourcing a word-emotion association lexicon. Comput. Intell. 29, 436–465 (2013).
doi: 10.1111/j.1467-8640.2012.00460.x
Buechel, S. & Hahn, U. EmoBank: Studying the impact of annotation perspective and representation format on dimensional emotion analysis. in Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Vol. 2, Short Papers. 578–585 (2017).
Goel, P., Kulshreshtha, D., Jain, P. & Shukla, K. K. Prayas at EmoInt 2017: An ensemble of deep neural architectures for emotion intensity prediction in tweets. in Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. 58–65 (Association for Computational Linguistics, 2017).
Elliott-Maksymowicz, K., Nikolaev, A. & Porpora, D. How much can you say in a tweet? An approach to political argumentation on twitter. Hum. Soc. Sci. Commun. 8, 1–9 (2021).
Cheng, M., Yin, C., Nazarian, S. & Bogdan, P. Deciphering the laws of social network-transcendent covid-19 misinformation dynamics and implications for combating misinformation phenomena. Sci. Rep. 11, 1–14 (2021).
Cheng, M., Li, Y., Nazarian, S. & Bogdan, P. From rumor to genetic mutation detection with explanations: A Gan approach. Sci. Rep. 11, 1–14 (2021).
Turiel, J., Fernandez-Reyes, D. & Aste, T. Wisdom of crowds detects covid-19 severity ahead of officially available data. Sci. Rep. 11, 1–9 (2021).
doi: 10.1038/s41598-021-93042-w
Standard Occupational Classification 2010 (soc2010). https://webarchive.nationalarchives.gov.uk/ukgwa/20160106024159/ . http://www.ons.gov.uk/ons/guide-method/classifications/current-standard-classifications/soc2010/index.html . Accessed 21 Aug 2021.
Cai, Y. et al. Recurrent neural network with pooling operation and attention mechanism for sentiment analysis: A multi-task learning approach. Knowl.-Based Syst. 203, 1–12 (2020).
doi: 10.1016/j.knosys.2020.105856
Haynes, W. Student’s t-test. in Encyclopedia of Systems Biology. 2023–2025 (2013).
Gupta, R. K., Vishwanath, A. & Yang, Y. Global reactions to covid-19 on twitter: A labelled dataset with latent topic, sentiment and emotion attributes. Preprint at: arXiv:2007.06954 (2020).
McNemar, Q. Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika 12, 153–157 (1947).
pubmed: 20254758
doi: 10.1007/BF02295996
Levine, R. V. Time and culture. California State University, Fresno. http://nobaproject.com/modules/time-and-culture . Accessed Nov 2020.
Bergadaa, M. M. The role of time in the action of the consumer. J. Consum. Res. 17, 289–302 (1990).
doi: 10.1086/208558
Gimpel, K. et al. Part-of-speech tagging for Twitter: Annotation, features, and experiments. in The 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. 42–47 (2011).
Suykens, J. A. & Vandewalle, J. Least squares support vector machine classifiers. Neural Process. Lett. 9, 293–300 (1999).
doi: 10.1023/A:1018628609742
Hochreiter, S. & Schmidhuber, J. Long short-term memory. Neural Comput. 9, 1735–1780 (1997).
pubmed: 9377276
doi: 10.1162/neco.1997.9.8.1735
Bach, S. H., He, B., Ratner, A. & Ré, C. Learning the structure of generative models without labeled data. in Proceedings of the 34th International Conference on Machine Learning. Vol. 70 . 273–282 (2017).
Ratner, A. et al. Snorkel: Rapid training data creation with weak supervision. VLDB J. 29, 709–730 (2020).
pubmed: 32214778
doi: 10.1007/s00778-019-00552-1
Pennington, J., Socher, R. & Manning, C. Glove: Global vectors for word representation. in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). 1532–1543 (2014).
Chung, J., Gulcehre, C., Cho, K. & Bengio, Y. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014).
O’Shea, K. & Nash, R. An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458 (2015).
Gee, G. & Wang, E. psyML at SemEval-2018 task 1: Transfer learning for sentiment and emotion analysis. in Proceedings of The 12th International Workshop on Semantic Evaluation. 369–376 (Association for Computational Linguistics, 2018). https://www.aclweb.org/anthology/S18-1056 .