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This study explores the association between music preferences and moral values by applying text analysis techniques to lyrics.
R. Plutchik, “A psychoevolutionary theory of emotions,” 1982
1982
Earlier work this paper cites.
P. J. Rentfrow and S. D. Gosling, “The do re mi’s of everyday life: the structure and personality correlates of music preferences.” Journal of personality and social psychology , vol. 84, no. 6, p. 1236, 2003
2003
Earlier work this paper cites.
D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent dirichlet allocation,” Journal of machine Learning research , vol. 3, no. Jan, pp. 993–1022, 2003
2003
Earlier work this paper cites.
J. Haidt and C. Joseph, “Intuitive ethics: How innately prepared intuitions generate culturally variable virtues,” Daedalus , vol. 133, no. 4, pp. 55–66, 2004
2004
Earlier work this paper cites.
S. O. Ali and Z. F. Peynircioğlu, “Songs and emotions: are lyrics and melodies equal partners?” Psychology of music , vol. 34, no. 4, pp. 511–534, 2006
2006
Earlier work this paper cites.
D. P. McAdams and J. L. Pals, “A new big five: fundamental principles for an integrative science of personality.” American psychologist , vol. 61, no. 3, p. 204, 2006
2006
Earlier work this paper cites.
J. Haidt and J. Graham, “When morality opposes justice: Conservatives have moral intuitions that liberals may not recognize,” Social Justice Research , vol. 20, no. 1, pp. 98–116, 2007
2007
Earlier work this paper cites.
E. J. Lightman, P. M. McCarthy, D. F. Dufty, and D. S. McNamara, “Using computational text analysis tools to compare the lyrics of suicidal and non-suicidal songwriters,” in Proceedings of the Annual Meeting of the Cognitive Science Society , vol. 29, no. 29, 2007
2007
Earlier work this paper cites.
J. Graham, J. Haidt, and B. A. Nosek, “Liberals and conservatives rely on different sets of moral foundations.” Journal of personality and social psychology , vol. 96, no. 5, p. 1029, 2009
2009
Earlier work this paper cites.
G. Lakoff, Moral politics: How liberals and conservatives think . University of Chicago Press, 2010
2010
Earlier work this paper cites.
R. Hu and P. Pu, “Enhancing collaborative filtering systems with personality information,” in Proceedings of the fifth ACM conference on Recommender systems , 2011, pp. 197–204
2011
Earlier work this paper cites.
C. N. DeWall, R. S. Pond Jr, W. K. Campbell, and J. M. Twenge, “Tuning in to psychological change: Linguistic markers of psychological traits and emotions over time in popular us song lyrics.” Psychology of Aesthetics, Creativity, and the Arts , vol. 5, no. 3, p. 200, 2011
2011
Earlier work this paper cites.
J. R. Ogden, D. T. Ogden, and K. Long, “Music marketing: A history and landscape,” Journal of Retailing and Consumer Services , vol. 18, no. 2, pp. 120–125, 2011
2011
Earlier work this paper cites.
A. Gardikiotis and A. Baltzis, “‘rock music for myself and justice to the world!’: Musical identity, values, and music preferences,” Psychology of Music , vol. 40, no. 2, pp. 143–163, 2012
2012
Earlier work this paper cites.
V. Swami, F. Malpass, D. Havard, K. Benford, A. Costescu, A. Sofitiki, and D. Taylor, “Metalheads: The influence of personality and individual differences on preference for heavy metal.” Psychology of Aesthetics, Creativity, and the Arts , vol. 7, no. 4, p. 377, 2013
2013
Earlier work this paper cites.
C. Loersch and N. L. Arbuckle, “Unraveling the mystery of music: Music as an evolved group process.” Journal of Personality and Social Psychology , vol. 105, no. 5, p. 777, 2013
2013
Earlier work this paper cites.
S. M. Mohammad and P. D. Turney, “Crowdsourcing a word–emotion association lexicon,” Computational intelligence , vol. 29, no. 3, pp. 436–465, 2013
2013
Earlier work this paper cites.
J. Graham, J. Haidt, S. Koleva, M. Motyl, R. Iyer, S. P. Wojcik, and P. H. Ditto, “Moral foundations theory: The pragmatic validity of moral pluralism,” in Advances in experimental social psychology . Elsevier, 2013, vol. 47, pp. 55–130
2013
Earlier work this paper cites.
E. Kim, R. Iyer, J. Graham, Y.-H. Chang, and R. Maheswaran, “Moral values from simple game play,” in International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction . Springer, 2013, pp. 56–64
2013
Earlier work this paper cites.
A. Laplante, “Improving music recommender systems: What can we learn from research on music tastes?” in ISMIR , 2014, pp. 451–456
2014
Earlier work this paper cites.
C. Hutto and E. Gilbert, “Vader: A parsimonious rule-based model for sentiment analysis of social media text,” in Proceedings of the international AAAI conference on web and social media , vol. 8, no. 1, 2014, pp. 216–225
2014
Cited alongside, same era.
M. Kosinski, Y. Bachrach, P. Kohli, D. Stillwell, and T. Graepel, “Manifestations of user personality in website choice and behaviour on online social networks,” Machine learning , vol. 95, no. 3, pp. 357–380, 2014
2014
Cited alongside, same era.
S. Sasaki, K. Yoshii, T. Nakano, M. Goto, and S. Morishima, “Lyricsradar: A lyrics retrieval system based on latent topics of lyrics.” in Ismir , 2014, pp. 585–590
2014
Cited alongside, same era.
M. Munezero, C. S. Montero, E. Sutinen, and J. Pajunen, “Are they different? affect, feeling, emotion, sentiment, and opinion detection in text,” IEEE transactions on affective computing , vol. 5, no. 2, pp. 101–111, 2014
2014
Cited alongside, same era.
L. Qiu, J. Chen, J. Ramsay, and J. Lu, “Personality predicts words in favorite songs,” Journal of Research in Personality , vol. 78, pp. 25–35, 2019
2019
Later among the works it cites.
Y. Mejova and K. Kalimeri, “Effect of values and technology use on exercise: implications for personalized behavior change interventions,” in Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization , 2019, pp. 36–45
2019
Later among the works it cites.
K. Kalimeri, M. G. Beiró, A. Urbinati, A. Bonanomi, A. Rosina, and C. Cattuto, “Human values and attitudes towards vaccination in social media,” in Companion Proceedings of The 2019 World Wide Web Conference , 2019, pp. 248–254
2019
Later among the works it cites.
K. Kalimeri, M. G. Beiró, M. Delfino, R. Raleigh, and C. Cattuto, “Predicting demographics, moral foundations, and human values from digital behaviours,” Comput. Hum. Behav. , vol. 92, pp. 428–445, 2019
2019
Later among the works it cites.
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A. Miles and S. Vaisey, “Morality and politics: Comparing alternate theories,” Social Science Research , vol. 53, pp. 252–269, 2015
2015
Cited alongside, same era.
A. Lancichinetti, M. I. Sirer, J. X. Wang, D. Acuna, K. Körding, and L. A. N. Amaral, “High-reproducibility and high-accuracy method for automated topic classification,” Physical Review X , vol. 5, no. 1, p. 011007, 2015
2015
Cited alongside, same era.
M. Röder, A. Both, and A. Hinneburg, “Exploring the space of topic coherence measures,” in WSDM , 2015, pp. 399–408
2015
Cited alongside, same era.
D. M. Greenberg, M. Kosinski, D. J. Stillwell, B. L. Monteiro, D. J. Levitin, and P. J. Rentfrow, “The song is you: Preferences for musical attribute dimensions reflect personality,” Social Psychological and Personality Science , vol. 7, no. 6, pp. 597–605, 2016
2016
Cited alongside, same era.
C. Wolsko, H. Ariceaga, and J. Seiden, “Red, white, and blue enough to be green: Effects of moral framing on climate change attitudes and conservation behaviors,” Journal of Experimental Social Psychology , vol. 65, pp. 7–19, 2016
2016
Cited alongside, same era.
A. B. Amin, R. A. Bednarczyk, C. E. Ray, K. J. Melchiori, J. Graham, J. R. Huntsinger, and S. B. Omer, “Association of moral values with vaccine hesitancy,” Nature Human Behaviour , vol. 1, no. 12, pp. 873–880, 2017
2017
Cited alongside, same era.
M. Honnibal and I. Montani, “spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing,” 2017, to appear
2017
Cited alongside, same era.
E. Çano and M. Morisio, “Moodylyrics: A sentiment annotated lyrics dataset,” in Proceedings of the 2017 International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence , 2017, pp. 118–124
2017
Cited alongside, same era.
I.Stat, “Italian statistics,” http://dati.istat.it/ , accessed: 2019-09
2019
Later among the works it cites.
M. Neumann, D. King, I. Beltagy, and W. Ammar, “ScispaCy: Fast and robust models for biomedical natural language processing,” in Proceedings of the 18th BioNLP Workshop and Shared Task . Florence, Italy: Association for Computational Linguistics, Aug. 2019, pp. 319–327. [Online]. Available: https://aclanthology.org/W19-5034
2019
Later among the works it cites.
Y. Jin, N. Tintarev, N. N. Htun, and K. Verbert, “Effects of personal characteristics in control-oriented user interfaces for music recommender systems,” User Modeling and User-Adapted Interaction , vol. 30, no. 2, pp. 199–249, 2020
2020
Later among the works it cites.
A. Urbinati, K. Kalimeri, A. Bonanomi, A. Rosina, C. Cattuto, and D. Paolotti, “Young adult unemployment through the lens of social media: Italy as a case study,” in International Conference on Social Informatics . Springer, 2020, pp. 380–396
2020
Later among the works it cites.
O. Araque, L. Gatti, and K. Kalimeri, “Moralstrength: Exploiting a moral lexicon and embedding similarity for moral foundations prediction,” Knowledge-based systems , vol. 191, p. 105184, 2020
2020
Later among the works it cites.
K. J. Messick and B. E. Aranda, “The role of moral reasoning & personality in explaining lyrical preferences,” PLoS one , vol. 15, no. 1, p. e0228057, 2020
2020
Later among the works it cites.
D. M. Greenberg, S. C. Matz, H. A. Schwartz, and K. R. Fricke, “The self-congruity effect of music.” Journal of Personality and Social Psychology , 2020
2020
Later among the works it cites.
L. Misael, C. Forster, E. Fontelles, V. Sampaio, and M. França, “Temporal analysis and visualisation of music,” in Anais do XVII Encontro Nacional de Inteligência Artificial e Computacional . SBC, 2020, pp. 507–518
2020
Later among the works it cites.
A. Anderson, L. Maystre, I. Anderson, R. Mehrotra, and M. Lalmas, “Algorithmic effects on the diversity of consumption on spotify,” in Proceedings of The Web Conference 2020 , 2020, pp. 2155–2165
2020
Later among the works it cites.
L. Porcaro, C. Castillo, and E. Gómez Gutiérrez, “Diversity by design in music recommender systems,” Transactions of the International Society for Music Information Retrieval. 2021; 4 (1). , 2021
2021
Later among the works it cites.
I. Anderson, S. Gil, C. Gibson, S. Wolf, W. Shapiro, O. Semerci, and D. M. Greenberg, ““just the way you are”: Linking music listening on spotify and personality,” Social Psychological and Personality Science , vol. 12, no. 4, pp. 561–572, 2021
2021
Later among the works it cites.
P. E. Savage, P. Loui, B. Tarr, A. Schachner, L. Glowacki, S. Mithen, and W. T. Fitch, “Music as a coevolved system for social bonding,” Behavioral and Brain Sciences , vol. 44, 2021
2021
Later among the works it cites.
J. H. Lee, A. Bhattacharya, R. Antony, N. K. Santero, and A. Le, “Finding home: Understanding how music supports listener mental health through a case study of bts,” in ISMIR , 2021, pp. 358–365
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
L. Sust, G. Kudchadker, R. Schoedel, T. Schuwerk, M. Bühner, and C. Stachl, “Personality computing with naturalistic music listening data,” PsyArXiv Preprints , 2022
2022
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