Sentiment analysis is a computational method that automatically analyzes the valence of massive quantities of text. Basic sentiment analysis involves extracting and counting emotionally-laden keywords from passages of text (e.g., hate, love, happy, sad). This study describes using sentiment analysis to explore changes in emotion expression in a developmental context. A sample of n = 8,688 poems published online by children and adolescents from Grade 4 to Grade 12 was analyzed. Sentiment analysis coded words as positive or negative and these were averaged within each poem to obtain its relative percentage of positive and negative sentiment. Polynomial regressions explored linear and nonlinear trends in sentiment scores by grade. Among the results, negative sentiment demonstrated an upward curvilinear trend, increasing sharply from Grade 6 to Grade 11 and then decreasing afterward. Positive sentiment demonstrated a sinusoidal pattern throughout development. Overall, these findings are consistent with previous research on the progressions of emotion expression in childhood and adolescence. Despite some limitations, sentiment analysis presents an opportunity for researchers in developmental psychology to explore basic questions in emotional development using large quantities of data.
ArnettJ. J. (1999). Adolescent storm and stress, reconsidered. American Psychologist, 54, 317–326.
2.
BarnesG. M.HoffmanJ. H.WelteJ. W.FarrellM. P.DintcheffB. A. (2007). Adolescents’ time use: Effects of substance use, delinquency, and sexual activity. Journal of Youth and Adolescence, 36, 697–710. doi:https://doi.org/10.1007/s10964-006-9075-0.
3.
BelfiA. M.VesselE. A.StarrG. G. (2018). Individual ratings of vividness predict aesthetic appeal in poetry. Psychology of Aesthetics, Creativity, and the Arts, 12, 341–350. doi:http://dx.doi.org/10.1037/aca0000153.
4.
BingL. (2015). Sentiment analysis. Cambridge, UK: Cambridge University Press.
5.
BorensztajnG.ZuidemaW.BodR. (2009). Children’s grammars grow more abstract with age – evidence from an automatic procedure for identifying the productive units of language. Topics in Cognitive Science, 1, 175–188. doi:10.1111/j.1756-8765.2008.01009.x.
6.
ColtheartM. (1981). The MRC psycholinguistic database. Quarterly Journal of Experimental Psychology, 33, 497–505.
7.
DemorestA.SilbersteinL.GardnerH.WinnerE. (1983). Telling it as it isn’t: Children’s understanding of figurative language. British Journal of Developmental Psychology, 1, 121–134.
8.
Devine-WrightP.Devine-WrightH.FlemingP. (2004). Situational influences upon children’s beliefs about global warming and energy. Environmental Education Research, 10, 493–506. doi:10.1080/1350462042000291029.
9.
ElkindD. (1967). Egocentrism in adolescence. Child Development, 38, 1025–1034.
10.
ForbesE. E.DahlR. E. (2010). Pubertal development and behavior: Hormonal activation of social and motivational tendencies. Brain and Cognition, 72, 66–72. doi:10.1016/j.bandc.2009.10.007.
11.
GarberJ.KeileyM. K.MartinN. C. (2002). Developmental trajectories of adolescents’ depressive symptoms: Predictors of change. Journal of Consulting and Clinical Psychology, 70, 79–95. doi:http://dx.doi.org/10.1037/0022-006X.70.1.79
12.
HaT.DishionT. J.OverbeekG.BurkW. J.EngelsR. C. M. E. (2014). The blues of adolescent romance: Observed affective interactions in adolescent romantic relationships associated with depressive symptoms. Journal of Abnormal Child Psychology, 42, 551–562. doi:10.1007/s10802-013-9808-y.
13.
HollensteinT.LougheedJ. P. (2013). Beyond storm and stress: Typicality, transactions, timing, and temperament to account for adolescent change. American Psychologist, 68, 444–454. doi:10.1037/a0033586.
JungH.ParkH. A.SongT. M. (2017). Ontology-based approach to social data sentiment analysis: Detection of adolescent depression signals. Journal of Medical Internet Research, 19, e259. doi:10.2196/jmir.7452.
16.
KalchbrennerN.GrefenstetteE.BlunsomP. (2014). A convolutional neural network for modelling sentences. Proceedings of ACL2014.
17.
LarsonR. W.MonetaG.RichardsM. H.WilsonS. (2002). Continuity, stability, and change in daily emotional experience across adolescence. Child Development, 73, 1151–1165. doi:https://doi.org/10.1111/1467-8624.00464.
18.
LaursenB.CoyK. C.CollinsW. A. (1998). Reconsidering changes in parent-child conflict across adolescence: A meta-analysis. Child Development, 69, 817–832.
19.
LewisD. D.YangY.RoseT. G.LiF. (2004). RCV1: A new benchmark collection for text categorization research. Journal of Machine Learning Research, 5, 361–397.
20.
MillerG. A. (1995). WordNet: A lexical database for English. Communications of the ACM, 38, 39–41.
21.
MohammadS. M. (2012). From once upon a time to happily ever after: Tracking emotions in mail and books. Decision Support Systems, 53, 730–741. doi:10.1016/j.dss.2012.05.030.
22.
MohammadS. M. (2018). Obtaining reliable human ratings of valence, arousal, and dominance for 20,000 English words. Proceedings of the Annual Conference for the Association for Computational Linguistics, Melbourne, Australia.
23.
MohammadS. M.Bravo-MarquezF.SalamehM.KiritchenkoS. (2018). SemEval-2018 Task 1: Affect in Tweets. Proceedings of International Workshop on Semantic Evaluation, New Orleans, LA.
24.
MohammadS. M.KiritchenkoS.ZhuX. (2013). NRC-Canada: Building the state-of-the-art in sentiment analysis of tweets. Proceedings of the Seventh International Workshop on Semantic Evaluation, SemEval’13, Atlanta, GA, pp. 321–327.
25.
MohammadS. M.TurneyP. D. (2010). Emotions evoked by common words and phrases: Using mechanical turk to create an emotion lexicon. Proceedings of the NAACL-HLT Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, Los Angeles, CA.
26.
MoranP.CoffeyC.RomaniukH.OlssonC.BorschmannR.CarlinJ. B.PattonG. C. (2012). The natural history of self-harm from adolescence to young adulthood: A population-based cohort study. Lancet, 379, 236–243. doi:https://doi.org/10.1016/S0140-6736(11)61141-0
27.
PonsF.HarrisP. (2005). Longitudinal change and longitudinal stability of individual differences in children’s emotion understanding. Cognition and Emotion, 19, 1158–1174. doi:10.1080/02699930500282108
28.
R Core Team. (2018). R: A language and environment for statistical computing. Vienna, Australia: R Foundation for Statistical Computing.
29.
RoeserR. W.EcclesJ. S.SameroffA. J. (2000). School as a context of early adolescents’ academic and social-emotional development: A summary of research findings. Elementary School Journal, 100, 443–471.
30.
RillS.ReinelD.ScheidtJ.ZicariR. V. (2014). PoliTwi: Early detection of emerging political topics on twitter and the impact on concept-level sentiment analysis. Knowledge-Based Systems, 69, 24–33. doi:https://doi.org/10.1016/j.knosys.2014.05.008.
31.
SchmidH. (1994). Probabilistic part-of-speech tagging using decision trees. Proceedings of International Conference on New Methods in Language Processing, Manchester, UK.
SilgeJ.RobinsonD. (2017). Text mining with R: A tidy approach. Sebastopol, CA: O’Reilly Media.
34.
SilgeJ.RobinsonD. (2018). tidytext: Text mining for word processing and sentiment analysis using ‘dplyr’, ‘ggplot2’ and other tidy tools(R package version 0.2.0).
35.
SimmonsR. G.BurgesonR.Carlton-FordS.BlythD. A. (1987). The impact of cumulative change in early adolescence. Child Development, 58, 1220–1234.
36.
SomervilleL. H. (2013). The teenage brain: Sensitivity to social evaluation. Current Directions in Psychological Science, 22, 129–135. doi:10.1177/0963721413476512
37.
SteinbergL.MorrisA. S. (2001). Adolescent development. Annual Review of Psychology, 52, 83–110.
38.
TousignantB.SiroisK.AchimA. M.MassicotteE.JacksonP. L. (2017). A comprehensive assessment of social cognition from adolescence to adulthood. Cognitive Development, 43, 214–223. doi:https://doi.org/10.1016/j.cogdev.2017.05.001
39.
WeinsteinS. M.MermelsteinR. J.HankinB. L.HedekerD.FlayB. R. (2007). Longitudinal patterns of daily affect and global mood during adolescence. Journal of Research in Adolescence, 17, 587–600. doi:10.1111/j.1532-7795.2007.00536.x
40.
WickhamH. (2018). ggplot2: Create elegant data visualizations using the grammar of graphics(R package version 3.1.0).
41.
WickhamH. (2018). dplyr: A grammar of data manipulation(R package version 0.7.8).
42.
WormwoodJ. B.DevlinM.LinY.-R.BarrettL. F.QuigleyK. S. (2018). When words hurt: Affective word use in daily news coverage impacts mental health. Frontiers in Psychology, 9, 1–10. doi:10.3389/fpsyg.2018.01333
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
