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Scientific Study of Literature
Vol. 7:2 (2017) ► pp. 232256
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 http://​research​.microsoft​.com​/pubs​/65430​/new​_domain​_sentiment​.pdf
Abe, J. A.
(2016) A longitudinal follow-up study of happiness and meaning-making. Journal of Positive Psychology, 111, 489–489. CrossrefGoogle Scholar
Bishop, J.
(1959) Wordsworth and the “spots of time.” ELH, 261, 45–65. CrossrefGoogle 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. CrossrefGoogle 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. CrossrefGoogle 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. CrossrefGoogle 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. Crossref.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. CrossrefGoogle Scholar
Cambria, E., Grassi, M., Hussain, A., & Havasi, C.
(2012) Sentic computing for social media marketing. Multimedia Tools and Applications, 591, 557–557. Crossref.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. CrossrefGoogle Scholar
Campbell, D. T., & Fiske, D. W.
(1959) Convergent and discriminant validation by the multitrait-multimethod matrix. Psychological Bulletin, 56(2), 81–105. CrossrefGoogle 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. CrossrefGoogle 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 www​.journalofwritingassessment​.org​/article​.php​?article​=80
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. CrossrefGoogle 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. CrossrefGoogle 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. CrossrefGoogle 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. CrossrefGoogle 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. CrossrefGoogle Scholar
Felluga, D.
(2017) COVE: Central Online Victorian Educator. Retrieved from https://​editions​.covecollective​.org/
Ferguson, C. J.
(2009) An effect size primer: A guide for clinicians and researchers. Professional Psychology: Research and Practice, 401, 532–538. CrossrefGoogle 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. CrossrefGoogle 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. CrossrefGoogle 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. CrossrefGoogle Scholar
Graham, S., & Perin, D.
(2007) A meta-analysis of writing instruction for adolescent students. Journal of Educational Psychology, 991, 445–476. CrossrefGoogle 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 http://​litlab​.stanford​.edu​/LiteraryLabPamphlet4​.pdf
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. CrossrefGoogle 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. CrossrefGoogle Scholar
Kousta, S., Vigliocco, G., Vinson, D. P., Andrews, M., & Campo, del E.
(2011) Journal of Experimental Psychology: General, 1401, 14–34. CrossrefGoogle 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. CrossrefGoogle 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. CrossrefGoogle 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. CrossrefGoogle Scholar
Markowitz, D. M., & Hancock, J. T.
(2016) Linguistic obfuscation in fraudulent science. Journal of Language & Social Psychology, 35(4), 435–445. CrossrefGoogle 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. CrossrefGoogle 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. CrossrefGoogle 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. CrossrefGoogle Scholar
Nowviskie, B.
(2015) Digital Humanities in the Anthropocene. Digital Scholarship in the Humanities, 301, i4–i15. CrossrefGoogle 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. CrossrefGoogle 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: Scholar
Phillips, M. G., & Osmond, G.
(2015) Australia’s women surfers: History, methodology and the digital humanities. Australian Historical Studies, 46(2), 285–303. CrossrefGoogle 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. CrossrefGoogle Scholar
Poole, A. H.
(2017) The conceptual ecology of digital humanities. Journal of Documentation, 731, 91–91. Crossref.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. Crossref.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. CrossrefGoogle Scholar
Scherer, K. R.
(2005) What are emotions? And how can they be measured? Social Science Information, 441, 695–729. CrossrefGoogle Scholar
Smyth, J.
(1998) Written emotional expression: Effect sizes, outcome types, and moderating variables. Journal of Consulting and Clinical Psychology, 661, 174–184. CrossrefGoogle 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. CrossrefGoogle 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. CrossrefGoogle Scholar
Wordsworth, W.
(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 https://​www​.gutenberg​.org/
Wuensch, K.
(2008) Two group discriminant function analysis. Retrieved from http://​core​.ecu​.edu
Yarkoni, T.
(2012) Psychoinformatics: New horizons at the interface of the psychological and computing sciences. Current Directions in Psychological Science, 211, 391–391. CrossrefGoogle 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. CrossrefGoogle 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. CrossrefGoogle Scholar