Cover not available
Article published In:
Scientific Study of Literature
Vol. 7:2 (2017) ► pp.232–256

Prelude as lifespan gauge

Quantifying psychological processes over time

 | Learning Evaluation and Assessment Psychology
Through multiple versions of Prelude, readers can follow the progress of a poem 41 years in the making, a period that exceeds by far the timeline of its narration. To do so I employed automated analysis platforms LIWC (Pennebaker, Chung, Ireland, Gonzales, & Booth, 2007a) and SEANCE (Crossley, Kyle, & McNamara, 2016). Results reveal that Wordsworth exhibited habits of mind resonant with maturation, especially in his increased positivity and abstraction. Discriminant function analysis revealed four psychological markers that almost completely identified shifts between editions. Indices connoting trust and sadness, as well as positive adjectives and the cognitive indicator of exclusion, accounted for 63 percent of the variance. The study offers a methodology for considering multiple versions of any text in which the passage of time becomes an important marker. I present these findings within a digital humanities framework and conclude by discussing applications.
Keywords: psychoinformatics, Wordsworth, digital humanities, sentiment analysis
Article outline
  • Wordsworth’s preoccupation with time
  • Prelude examined through the digital humanities
  • Research questions
  • Method
    • Corpus
    • Instruments
    • Procedures
    • Statistical analysis
      • Concurrent validity
      • Establishing tendencies of the meta-Prelude
  • Results
    • Q1
    • Q2
      • Magnitudes of difference
      • Discriminant function analysis
  • Discussion
  • Future study and cautions
  • Acknowledgements
  • References
Published online: 28 June 2018
DOI logo
https://doi.org/10.1075/ssol.16023.tho
References (75)
References
Aue, A., & Gamon, M. (2005). Customizing sentiment classifiers to new domains: A case study. In Proceedings of Recent Advances in Natural Language Processing. Retrieved from [URL]
Abe, J. A. (2016). A longitudinal follow-up study of happiness and meaning-making. Journal of Positive Psychology, 111, 489–489. DOI logoGoogle Scholar
Bishop, J. (1959). Wordsworth and the “spots of time.” ELH, 261, 45–65. DOI logoGoogle Scholar
Blitzer, J., Dredze, M., & Pereira, F. (June, 2007). Biographies, Bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. Paper presented at the conference of the Association of Computational Linguistics, Prague, Czech Republic.
Bottou, L., Haffner, P., Howard, P. G., Simard, P., Bengio, Y., & le Cun, Y. (1998). High quality document image compression with DjVu. Journal of Electronic Imaging, 71, 410–425. DOI logoGoogle Scholar
Branch, L. (2006). Rituals of spontaneity: Sentiment and secularism from free prayer to Wordsworth. Waco, TX: Baylor University Press.Google Scholar
Brown, M. T., & Wicker, L. R. (2000). Discriminant analysis. In H. E. A. Tinsley & S. D. Brown (Eds.), Handbook of applied multivariate statistics and mathematical modeling (pp. 209–235). New York, NY: Academic Press. DOI logoGoogle Scholar
Brown, N. M., Mendenhall, R., Black, M. L., Moer, M. V., Zerai, A., & Flynn, K. (2016). Mechanized margin to digitized center: Black feminism’s contributions to combatting erasure within the digital humanities. International Journal of Humanities & Arts Computing: A Journal Of Digital Humanities, 10(1), 110–125. DOI logoGoogle Scholar
Brose, A., Scheibe, S., & Schmiedek, F. (2013). Life contexts make a difference: Emotional stability in younger and older adults. Psychology and Aging, 28(1), 148–159. DOI logo.Google Scholar
Burman, J. T., Green, C. D., & Shanker, S. (2015). On the meanings of self-regulation: Digital humanities in service of conceptual clarity. Child Development, 861, 1507–1507. DOI logoGoogle Scholar
Cambria, E., Grassi, M., Hussain, A., & Havasi, C. (2012). Sentic computing for social media marketing. Multimedia Tools and Applications, 591, 557–557. DOI logo.Google Scholar
Cambria, E., Havasi, C., & Hussain, A. (2012). SenticNet 2: A semantic and affective resource for opinion mining and sentiment analysis. In G. M. Youngblood & P. M. Mcarthy (Eds.), Proceedings of the 25th Florida artificial intelligence research society conference (pp. 202–207). Palo Alto, CA: The Association for the Advancement of Artificial Intelligence Press.Google Scholar
Cambria, E., Hussain, A., & Xia, Y. (2012). Affective common sense knowledge acquisition for sentiment analysis. Paper presented at Language Resources and Evaluation, Istanbul.
Cambria, E., Poria, S., Gelbukh, A., & Thelwall, M. (2017). Sentiment analysis is a big suitcase. IEEE Intelligent Systems, 321, 74–80. DOI logoGoogle Scholar
Campbell, D. T., & Fiske, D. W. (1959). Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56(2), 81–105. DOI logoGoogle Scholar
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
(1992). A power primer. Psychological Bulletin, 1121, 155–159. DOI logoGoogle Scholar
Crossley, S. A., Kyle, K., & McNamara, D. S. (2015). To aggregate or not? Linguistic features in automatic essay scoring and feedback systems. Journal of Writing Assessment, 81, 80. Retrieved from [URL]
Crossley, S. A., Kyle, K., McNamara, D. S. (2016). Sentiment analysis and social cognition engine (SEANCE ): An automatic tool for sentiment, social cognition, and social-order analysis. Behavior Research Methods, 481. DOI logoGoogle Scholar
Davis, J. P. (1992). The “spots of time”: Wordsworth’s poetic debt to Coleridge. Colby Quarterly, 281, 65–84.Google Scholar
Diehl, M., Hay, E., & Berg, K. M. (2011). The ratio between positive and negative affect and flourishing mental health across adulthood. Aging & Mental Health, 15(7), 882–893. DOI logoGoogle Scholar
Donohue, W. A., Liang, Y., & Druckman, D. (2014). Validating LIWC dictionaries: The Oslo I Accords. Journal of Language & Social Psychology, 33(3), 282–301. DOI logoGoogle Scholar
Eijnatten, J. van., Pieters, T. & Verheul, J., (2013). Big data for global history: The transformative promise of digital humanities. BMGN-Low Countries Historical Review, 121, pp.55–77. DOI logoGoogle Scholar
Fagnani, C., Medda, E., Stazi, M., Caprara, G. V., & Alessandri, G. (2014). Investigation of age and gender effects on positive orientation in Italian twins. International Journal of Psychology, 491, 453–461. DOI logoGoogle Scholar
Felluga, D. (2017). COVE: Central Online Victorian Educator. Retrieved from [URL]
Ferguson, C. J. (2009). An effect size primer: A guide for clinicians and researchers. Professional Psychology: Research and Practice, 401, 532–538. DOI logoGoogle Scholar
Fernandez, K. C., Gordon, E. A., Rodebaugh, T. L., & Heimberg, R. G. (2016). Exploring linguistic correlates of social anxiety in romantic stories. Cognitive Behaviour Therapy, 451, 351–366. DOI logoGoogle Scholar
Fernández-Cabana, M., García-Caballero, A., Alves-Pérez, M. T., García-García, M. J., & Mateos, R. (2013). Suicidal traits in Marilyn Monroe’s fragments: An LIWC analysis. Crisis: The Journal of Crisis Intervention and Suicide Prevention, 34(2), 124–130. DOI logoGoogle Scholar
Field, A. (2005). Discovering statistics using SPSS (2nd ed.). Thousand Oaks, CA: Sage.Google Scholar
Graesser, A. C., McNamara, D. S., & Kulikowich, J. (2011). Coh-Metrix: Providing multilevel analyses of text characteristics. Educational Researcher, 401, 223–234. DOI logoGoogle Scholar
Graham, S., & Perin, D. (2007). A meta-analysis of writing instruction for adolescent students. Journal of Educational Psychology, 991, 445–476. DOI logoGoogle Scholar
Gravetter, F. J., & Wallnau, L. B. (2014). Statistics for the Behavioral Sciences. Belmont, CA: Cengage.Google Scholar
Heuser, R. & Le-Khac, L. (2012). A quantitative literary history of 2,958 nineteenth-century British novels: The semantic cohort method. Pamphlet 4 from Stanford Literary Lab. Retrieved from [URL]
Hu, M., & Liu, B. (2004). Mining and summarizing customer reviews. In W. Kim & R. Kohavi (Eds.), Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining (pp. 168–177). Washington, DC: Association for Computing Machinery Press.Google Scholar
Hutto, C. J., & Gilbert, E. (2014). Vader: A parsimonious rule-based model for sentiment analysis of social media text. In E. Adar & P. Resnick (Eds.), Proceedings of the eighth international AAAI conference on weblogs and social media (pp. 216–225). Palo Alto, CA: Association for the Advancement of Artificial Intelligence Press.Google Scholar
Kaldenberg, E. R., Ganzeveld, P., Hosp, J. L., & Rodgers, D. B. (2016). Common characteristics of writing interventions for students with learning disabilities: A synthesis of the literature. Psychology in the Schools, 531, 938–953. DOI logoGoogle Scholar
Kern, M. L., Eichstaedt, J. C., Schwartz, H. A., Park, G., Ungar, L. H., Stillwell, D. J., & Seligman, M. E. P. (2014). From “Sooo excited!!!” to “So proud”: Using language to study development. Developmental Psychology, 50(1), 178–188. DOI logoGoogle Scholar
Kousta, S., Vigliocco, G., Vinson, D. P., Andrews, M., & Campo, del E. (2011). Journal of Experimental Psychology: General, 1401, 14–34. DOI logoGoogle Scholar
Kurtz, M. M., Ragland, J. D., Moberg, P. J., & Gur, R. C. (2004). The Penn conditional exclusion test: A new measure of executive-function with alternate forms for repeat administration. Archives of Clinical Neuropsychology, 191, 191–201. DOI logoGoogle Scholar
Lasswell, H. D., & Namenwirth, J. Z. (1969). The Lasswell value dictionary. New Haven, CT: Yale University Press.Google Scholar
Lindenberger, H. (1963). On Wordsworth’s Prelude. Princeton, NJ: Princeton. DOI logoGoogle Scholar
Maher, J. M., Markey, J. C., & Ebert-May, D. (2013). The other half of the story: Effect size analysis in quantitative research. CBE – Life Sciences Education, 121, 345–351. DOI logoGoogle Scholar
Markowitz, D. M., & Hancock, J. T. (2016). Linguistic obfuscation in fraudulent science. Journal of Language & Social Psychology, 35(4), 435–445. DOI logoGoogle Scholar
Mast, M. S., Gatica-Perez, D., Frauendorfer, D., Nguyen, L., & Choudhury, T. (2015). Social sensing for psychology: Automated interpersonal behavior assessment. Current Directions in Psychological Science, 241, 154–160. DOI logoGoogle Scholar
Mohammad, S. M., & Turney, P. D. (2010). Emotions evoked by common words and phrases: Using mechanical Turk to create an emotion lexicon. In Proceedings of the NAACL HLT 2010 workshop on computational approaches to analysis and generation of emotion in text (pp. 26–34). Stroudsburg, PA: Association for Computational Linguistics.Google Scholar
(2013). Crowdsourcing a word – emotion association lexicon. Computational Intelligence, 291, 436–465. DOI logoGoogle Scholar
Moretti, G., Sprugnoli, R., Menini, S., & Tonelli, S. (2016). ALCIDE: Extracting and visualizing content from large document collections to support humanities studies. Knowledge-Based Systems, 1111, 100–112. DOI logoGoogle Scholar
Nowviskie, B. (2015). Digital Humanities in the Anthropocene. Digital Scholarship in the Humanities, 301, i4–i15. DOI logoGoogle Scholar
Ogden, J. T. (1975). The structure of imaginative experience in Wordsworth’s Prelude . The WordsworthCircle, 61, 290–98.Google Scholar
Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of the ACL-02 conference on empirical methods in natural language processing (pp. 79–86). Stroudsburg, PA: Associationfor Computational Linguistics.Google Scholar
(November, 2002). Thumbs up? Sentiment classification using machine learning techniques. Paper presented at Association for Computational Linguistics, Stroudsburg, PA.
Pennebaker, J. W., & Stone, L. D. (2003). Words of wisdom: Language use over the life span. Journal of Personality and Social Psychology, 851, 291–301. DOI logoGoogle Scholar
Pennebaker, J. W., C. K. Chung, Ireland, M., A. Gonzales, & Booth, R. J. (2007a). Linguistic inquiry and word count (LIWC) 2007. Austin: University of Texas.Google Scholar
Pennebaker, J. W., C. K. Chung, Ireland, M., Gonzales, A., & Booth, R. J. (2007b). The development and psychometric properties of LIWC2007. Austin, TX: LIWC.net.Google Scholar
Phillips, M. G., & Osmond, G. (2015). Australia’s women surfers: History, methodology and the digital humanities. Australian Historical Studies, 46(2), 285–303. DOI logoGoogle Scholar
Pilar Salas-Zárate, M. del, López-López, E., Valencia-García, R., Aussenac-Gilles, N., Almela, Á., & Alor-Hernández, G. (2014). A study on LIWC categories for opinion mining in Spanish reviews. Journal of Information Science, 401, 749–749. DOI logoGoogle Scholar
Poole, A. H. (2017). The conceptual ecology of digital humanities. Journal of Documentation, 731, 91–91. DOI logo.Google Scholar
Robinson, R. L., Navea, R., & Ickes, W. (2013). Predicting final course performance from students’ written self-introductions: A LIWC analysis. Journal of Language & Social Psychology, 321, 469–469. DOI logo.Google Scholar
Ruyskensvelde, S. van. (2014). Towards a history of e-ducation? Exploring the possibilities of digital humanities for the history of education. Paedagogica Historica, 50(6), 861–870. DOI logoGoogle Scholar
Scherer, K. R. (2005). What are emotions? And how can they be measured? Social Science Information, 441, 695–729. DOI logoGoogle Scholar
Smyth, J. (1998). Written emotional expression: Effect sizes, outcome types, and moderating variables. Journal of Consulting and Clinical Psychology, 661, 174–184. DOI logoGoogle Scholar
Stone, P., Dunphy, D. C., Smith, M. S., Ogilvie, D. M., & Associates. (1966). The general inquirer: A computer approach to content analysis. Cambridge: Massachusetts Institute of Technology Press.Google Scholar
Thomson, D. (2015). Lifespan development in the Academy of American Poets. Scientific Study of Literature, 51, 83–98. DOI logoGoogle Scholar
Trochim, W., & Donnelly, J. P. (2006). The research methods knowledge base. Belmont, CA: Cengage.Google Scholar
Wilkens, M. (2015). Digital humanities and its application in the study of literature and culture. Comparative Literature, 671, 11–11. DOI logoGoogle Scholar
Wordsworth, W. (1798). Tintern Abbey. Accessed March 20, 2017 from [URL]
(1802). My heart leaps up. Retrieved from [URL]
(1979). The prelude: 1799, 1805, 1850. New York, NY: Norton.Google Scholar
(1995). The two-part prelude. New York, NY: Penguin.Google Scholar
(2009). Letter to the Bishop of Llandaff. In W. J. B. Owens and J. W. Smyser (Eds.) Wordsworth’s political writings (pp. 13–58). Benton Harbor, MI: Ambis.Google Scholar
(2017). Two addresses to the Freeholders of Westmoreland. Retrieved from [URL]
Wuensch, K. (2008). Two group discriminant function analysis. Retrieved from [URL]
Yarkoni, T. (2012). Psychoinformatics: New horizons at the interface of the psychological and computing sciences. Current Directions in Psychological Science, 211, 391–391. DOI logoGoogle Scholar
Yu, Y., Duan, W., & Cao, Q. (2013). The impact of social and conventional media on firm equity value: A sentiment analysis approach. Decision Support Systems, 551, 919–926. DOI logoGoogle Scholar
Yu, X., Liu, Y., Huang, X., & An, A. (2012). Mining online reviews for predicting sales performance: A case study in the movie domain. IEEE Transactions on Knowledge and Data Engineering, 241, 720–734. DOI logoGoogle Scholar
Cited by (2)

Cited by two other publications

Junker, Christine R., Mark S. Cubberley & Stephen J. Jacquemin
2022. Swirling currents. Scientific Study of Literature 12:1-2 ► pp. 28 ff. DOI logo
Thomson, David
2022. The new monstrous and its resonance with Frankenstein . Scientific Study of Literature 12:1-2 ► pp. 4 ff. DOI logo

This list is based on CrossRef data as of 19 july 2024. Please note that it may not be complete. Sources presented here have been supplied by the respective publishers. Any errors therein should be reported to them.