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Volume 7 Number 2
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Abstract

Through multiple versions of , 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.

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2018-06-28
2025-04-28

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References

  1. Aue, A., & Gamon, M.
    (2005) Customizing sentiment classifiers to new domains: A case study. InProceedings of Recent Advances in Natural Language Processing. Retrieved fromresearch.microsoft.com/pubs/65430/new_domain_sentiment.pdf
     [Google Scholar]
  2. Abe, J. A.
    (2016) A longitudinal follow-up study of happiness and meaning-making. Journal of Positive Psychology, 11, 489–489. 10.1080/17439760.2015.1117129
     https://doi.org/10.1080/17439760.2015.1117129 [Google Scholar]
  3. Bishop, J.
    (1959) Wordsworth and the “spots of time.” ELH, 26, 45–65. 10.2307/2872079
     https://doi.org/10.2307/2872079 [Google Scholar]
  4. Blitzer, J., Dredze, M., & Pereira, F.
    (June 2007) Biographies, Bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. Paper presented at theconference of the Association of Computational Linguistics, Prague, Czech Republic.
     [Google Scholar]
  5. 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, 7, 410–425. 10.1117/1.482609
     https://doi.org/10.1117/1.482609 [Google Scholar]
  6. Branch, L.
    (2006) Rituals of spontaneity: Sentiment and secularism from free prayer to Wordsworth. Waco, TX: Baylor University Press.
     [Google Scholar]
  7. Brown, M. T., & Wicker, L. R.
    (2000) Discriminant analysis. InH. E. A. Tinsley & S. D. Brown (Eds.), Handbook of applied multivariate statistics and mathematical modeling (pp.209–235). New York, NY: Academic Press. 10.1016/B978‑012691360‑6/50009‑4
     https://doi.org/10.1016/B978-012691360-6/50009-4 [Google Scholar]
  8. 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. 10.3366/ijhac.2016.0163
     https://doi.org/10.3366/ijhac.2016.0163 [Google Scholar]
  9. 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. 10.1037/a0030047.
     https://doi.org/10.1037/a0030047 [Google Scholar]
  10. Burman, J. T., Green, C. D., & Shanker, S.
    (2015) On the meanings of self-regulation: Digital humanities in service of conceptual clarity. Child Development, 86, 1507–1507. 10.1111/cdev.12395
     https://doi.org/10.1111/cdev.12395 [Google Scholar]
  11. Cambria, E., Grassi, M., Hussain, A., & Havasi, C.
    (2012) Sentic computing for social media marketing. Multimedia Tools and Applications, 59, 557–557. 10.1007/s11042‑011‑0815‑0.
     https://doi.org/10.1007/s11042-011-0815-0 [Google Scholar]
  12. Cambria, E., Havasi, C., & Hussain, A.
    (2012) SenticNet 2: A semantic and affective resource for opinion mining and sentiment analysis. InG. 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]
  13. Cambria, E., Hussain, A., & Xia, Y.
    (2012) Affective common sense knowledge acquisition for sentiment analysis. Paper presented atLanguage Resources and Evaluation, Istanbul.
     [Google Scholar]
  14. Cambria, E., Poria, S., Gelbukh, A., & Thelwall, M.
    (2017) Sentiment analysis is a big suitcase. IEEE Intelligent Systems, 32, 74–80.
     [Google Scholar]
  15. Campbell, D. T., & Fiske, D. W.
    (1959) Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56(2), 81–105. 10.1037/h0046016
     https://doi.org/10.1037/h0046016 [Google Scholar]
  16. Cohen, J.
    (1988) Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.
     [Google Scholar]
  17. (1992) A power primer. Psychological Bulletin, 112, 155–159. 10.1037/0033‑2909.112.1.155
     https://doi.org/10.1037/0033-2909.112.1.155 [Google Scholar]
  18. 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, 8, 80. Retrieved fromwww.journalofwritingassessment.org/article.php?article=80
     [Google Scholar]
  19. 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, 48. 10.3758/s13428‑016‑0743‑z
     https://doi.org/10.3758/s13428-016-0743-z [Google Scholar]
  20. Davis, J. P.
    (1992) The “spots of time”: Wordsworth’s poetic debt to Coleridge. Colby Quarterly, 28, 65–84.
     [Google Scholar]
  21. 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. 10.1080/13607863.2011.569488
     https://doi.org/10.1080/13607863.2011.569488 [Google Scholar]
  22. Donohue, W. A., Liang, Y., & Druckman, D.
    (2014) Validating LIWC dictionaries: The Oslo I Accords. Journal of Language & Social Psychology, 33(3), 282–301. 10.1177/0261927X13512485
     https://doi.org/10.1177/0261927X13512485 [Google Scholar]
  23. Eijnatten, J. van., Pieters, T. & Verheul, J.
    (2013) Big data for global history: The transformative promise of digital humanities. BMGN-Low Countries Historical Review, 12, pp.55–77. 10.18352/bmgn‑lchr.9350
     https://doi.org/10.18352/bmgn-lchr.9350 [Google Scholar]
  24. 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, 49, 453–461. 10.1002/ijop.12053
     https://doi.org/10.1002/ijop.12053 [Google Scholar]
  25. Felluga, D.
    (2017) COVE: Central Online Victorian Educator. Retrieved fromhttps://editions.covecollective.org/
  26. Ferguson, C. J.
    (2009) An effect size primer: A guide for clinicians and researchers. Professional Psychology: Research and Practice, 40, 532–538. 10.1037/a0015808
     https://doi.org/10.1037/a0015808 [Google Scholar]
  27. 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, 45, 351–366.
     [Google Scholar]
  28. 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. 10.1027/0227‑5910/a000183
     https://doi.org/10.1027/0227-5910/a000183 [Google Scholar]
  29. Field, A.
    (2005) Discovering statistics using SPSS (2nd ed.). Thousand Oaks, CA: Sage.
     [Google Scholar]
  30. Graesser, A. C., McNamara, D. S., & Kulikowich, J.
    (2011) Coh-Metrix: Providing multilevel analyses of text characteristics. Educational Researcher, 40, 223–234. 10.3102/0013189X11413260
     https://doi.org/10.3102/0013189X11413260 [Google Scholar]
  31. Graham, S., & Perin, D.
    (2007) A meta-analysis of writing instruction for adolescent students. Journal of Educational Psychology, 99, 445–476. 10.1037/0022‑0663.99.3.445
     https://doi.org/10.1037/0022-0663.99.3.445 [Google Scholar]
  32. Gravetter, F. J., & Wallnau, L. B.
    (2014) Statistics for the Behavioral Sciences. Belmont, CA: Cengage.
     [Google Scholar]
  33. Heuser, R. & Le-Khac, L.
    (2012) A quantitative literary history of 2,958 nineteenth-century British novels: The semantic cohort method. Pamphlet 4fromStanford Literary Lab. Retrieved fromlitlab.stanford.edu/LiteraryLabPamphlet4.pdf
     [Google Scholar]
  34. Hu, M., & Liu, B.
    (2004) Mining and summarizing customer reviews. InW. 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]
  35. Hutto, C. J., & Gilbert, E.
    (2014) Vader: A parsimonious rule-based model for sentiment analysis of social media text. InE. 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]
  36. 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, 53, 938–953.
     [Google Scholar]
  37. 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. 10.1037/a0035048
     https://doi.org/10.1037/a0035048 [Google Scholar]
  38. Kousta, S., Vigliocco, G., Vinson, D. P., Andrews, M., & Campo, del E.
    (2011) Journal of Experimental Psychology: General, 140, 14–34. 10.1037/a0021446
     https://doi.org/10.1037/a0021446 [Google Scholar]
  39. 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, 19, 191–201. 10.1016/S0887‑6177(03)00003‑9
     https://doi.org/10.1016/S0887-6177(03)00003-9 [Google Scholar]
  40. Lasswell, H. D., & Namenwirth, J. Z.
    (1969) The Lasswell value dictionary. New Haven, CT: Yale University Press.
     [Google Scholar]
  41. Lindenberger, H.
    (1963) On Wordsworth’s Prelude. Princeton, NJ: Princeton.
     [Google Scholar]
  42. 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, 12, 345–351.
     [Google Scholar]
  43. Markowitz, D. M., & Hancock, J. T.
    (2016) Linguistic obfuscation in fraudulent science. Journal of Language & Social Psychology, 35(4), 435–445. 10.1177/0261927X15614605
     https://doi.org/10.1177/0261927X15614605 [Google Scholar]
  44. 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, 24, 154–160. 10.1177/0963721414560811
     https://doi.org/10.1177/0963721414560811 [Google Scholar]
  45. Mohammad, S. M., & Turney, P. D.
    (2010) Emotions evoked by common words and phrases: Using mechanical Turk to create an emotion lexicon. InProceedings 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]
  46. (2013) Crowdsourcing a word – emotion association lexicon. Computational Intelligence, 29, 436–465. 10.1111/j.1467‑8640.2012.00460.x
     https://doi.org/10.1111/j.1467-8640.2012.00460.x [Google Scholar]
  47. 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, 111, 100–112.
     [Google Scholar]
  48. Nowviskie, B.
    (2015) Digital Humanities in the Anthropocene. Digital Scholarship in the Humanities, 30, i4–i15. 10.1093/llc/fqv015
     https://doi.org/10.1093/llc/fqv015 [Google Scholar]
  49. Ogden, J. T.
    (1975) The structure of imaginative experience in Wordsworth’s Prelude. The WordsworthCircle, 6, 290–98.
     [Google Scholar]
  50. Pang, B., Lee, L., & Vaithyanathan, S.
    (2002) Thumbs up? Sentiment classification using machine learning techniques. InProceedings of the ACL-02 conference on empirical methods in natural language processing (pp.79–86). Stroudsburg, PA: Associationfor Computational Linguistics.
     [Google Scholar]
  51. (November 2002) Thumbs up? Sentiment classification using machine learning techniques. Paper presented atAssociation for Computational Linguistics, Stroudsburg, PA.
     [Google Scholar]
  52. Pennebaker, J. W., & Stone, L. D.
    (2003) Words of wisdom: Language use over the life span. Journal of Personality and Social Psychology, 85, 291–301. 10.1037/0022‑3514.85.2.291
     https://doi.org/10.1037/0022-3514.85.2.291 [Google Scholar]
  53. 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]
  54. 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]
  55. Phillips, M. G., & Osmond, G.
    (2015) Australia’s women surfers: History, methodology and the digital humanities. Australian Historical Studies, 46(2), 285–303. 10.1080/1031461X.2015.1044757
     https://doi.org/10.1080/1031461X.2015.1044757 [Google Scholar]
  56. 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, 40, 749–749. 10.1177/0165551514547842
     https://doi.org/10.1177/0165551514547842 [Google Scholar]
  57. Poole, A. H.
    (2017) The conceptual ecology of digital humanities. Journal of Documentation, 73, 91–91. 10.1108/JD‑05‑2016‑0065.
     https://doi.org/10.1108/JD-05-2016-0065 [Google Scholar]
  58. 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, 32, 469–469. 10.1177/0261927X13476869.
     https://doi.org/10.1177/0261927X13476869 [Google Scholar]
  59. 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. 10.1080/00309230.2014.955511
     https://doi.org/10.1080/00309230.2014.955511 [Google Scholar]
  60. Scherer, K. R.
    (2005) What are emotions? And how can they be measured?Social Science Information, 44, 695–729. 10.1177/0539018405058216
     https://doi.org/10.1177/0539018405058216 [Google Scholar]
  61. Smyth, J.
    (1998) Written emotional expression: Effect sizes, outcome types, and moderating variables. Journal of Consulting and Clinical Psychology, 66, 174–184. 10.1037/0022‑006X.66.1.174
     https://doi.org/10.1037/0022-006X.66.1.174 [Google Scholar]
  62. 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]
  63. Thomson, D.
    (2015) Lifespan development in the Academy of American Poets. Scientific Study of Literature, 5, 83–98. 10.1075/ssol.5.1.04tho
     https://doi.org/10.1075/ssol.5.1.04tho [Google Scholar]
  64. Trochim, W., & Donnelly, J. P.
    (2006) The research methods knowledge base. Belmont, CA: Cengage.
     [Google Scholar]
  65. Wilkens, M.
    (2015) Digital humanities and its application in the study of literature and culture. Comparative Literature, 67, 11–11. 10.1215/00104124‑2861911
     https://doi.org/10.1215/00104124-2861911 [Google Scholar]
  66. Wordsworth, W.
    (1798) Tintern Abbey. AccessedMarch 20, 2017fromhttps://www.poetryfoundation.org/poems-and-poets/poems/detail/45527
  67. (1802) My heart leaps up. Retrieved fromhttps://www.poets.org/poetsorg/poem/my-heart-leaps
  68. (1979) The prelude: 1799, 1805, 1850. New York, NY: Norton.
     [Google Scholar]
  69. (1995) The two-part prelude. New York, NY: Penguin.
     [Google Scholar]
  70. (2009) Letter to the Bishop of Llandaff. InW. J. B. Owens and J. W. Smyser (Eds.) Wordsworth’s political writings (pp.13–58). Benton Harbor, MI: Ambis.
     [Google Scholar]
  71. (2017) Two addresses to the Freeholders of Westmoreland. Retrieved fromhttps://www.gutenberg.org/
     [Google Scholar]
  72. Wuensch, K.
    (2008) Two group discriminant function analysis. Retrieved fromcore.ecu.edu
  73. Yarkoni, T.
    (2012) Psychoinformatics: New horizons at the interface of the psychological and computing sciences. Current Directions in Psychological Science, 21, 391–391. 10.1177/0963721412457362
     https://doi.org/10.1177/0963721412457362 [Google Scholar]
  74. 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, 55, 919–926. 10.1016/j.dss.2012.12.028
     https://doi.org/10.1016/j.dss.2012.12.028 [Google Scholar]
  75. 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, 24, 720–734. 10.1109/TKDE.2010.269
     https://doi.org/10.1109/TKDE.2010.269 [Google Scholar]
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Prelude as lifespan gauge
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  • Article Type: Research Article
Keyword(s): digital humanities; psychoinformatics; sentiment analysis; Wordsworth

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