TACIT: An open-source text analysis, crawling, and interpretation tool

Behavior Research Methods

Volumn 49, Issue 2, 2017, Pages 538-547

  Dehghani, Morteza ^a   Johnson, Kate M ^a   Garten, Justin ^a   Boghrati, Reihane ^a   Hoover, Joe ^a   Balasubramanian, Vijayan ^a   Singh, Anurag ^a   Shankar, Yuvarani ^a   Pulickal, Linda ^a   Rajkumar, Aswin ^a   Parmar, Niki Jitendra ^a  

^a UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

Author keywords

Automated text analysis; Computational linguistics; Corpus analysis tools; Language based assessment

Indexed keywords

HUMAN; HUMAN EXPERIMENT; INFORMATION PROCESSING; SCIENTIST; DATA MINING; PROCEDURES; SOFTWARE;

DATA MINING; HUMANS; SOFTWARE;

EID: 84960130552     PISSN: 1554351X     EISSN: 15543528     Source Type: Journal    
DOI: 10.3758/s13428-016-0722-4     Document Type: Review

References (59)

1. Abell, M. (2014). SAS Text Miner [Software]. Available from www.sas.com/en_us/software/analytics/text-miner.html
2. Akthar, F., & Hahne, C. (2012). RapidMiner 5 operator reference. Cambridge: RapidMiner.
3. Andrzejewski, D., & Zhu, X. (2009). Latent Dirichlet allocation with topic-in-set knowledge. In Proceedings of the NAACL HLT 2009 Workshop on Semi-Supervised Learning for Natural Language Processing (pp. 43–48). Stroudsburg: Association for Computational Linguistics.
4. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.
5. Bouckaert, R. R., Frank, E., Hall, M. A., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2010). WEKA—Experiences with a Java open-source project. Journal of Machine Learning Research, 11, 2533–2541.
6. Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32.
7. Chang, C.-C., & Lin, C.-J. (2011). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent System Technology, 2, 27:1–27:27.
8. Cohn, M. A., Mehl, M. R., & Pennebaker, J. W. (2004). Linguistic markers of psychological change surrounding September 11, 2001. Psychological science, 15(10), 687–693.
9. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20, 273–297.
10. De Choudhury, M., Gamon, M., Counts, S., & Horvitz, E. (2013). Predicting depression via social media. In ICWSM 2013. Boston, MA: Association for the Advancement of Artificial Intelligence.
11. Deerwester, S., Dumais, S., Landauer, T., & Furnas, G. (1990). Indexing by latent semantic analysis. JASIS. Retrieved from www.cob.unt.edu/itds/faculty/evangelopoulos/dsci5910/LSA_Deerwester1990.pdf
12. Dehghani, M., Johnson, K. M., Hoover, J., Sagi, E., Garten, J., Parmar, N. J.,.. Graham, J. (2016). Purity homophily in social networks. Journal of Experimental Psychology: General, 145, 366–375. doi:10.1037/xge0000139
13. Dehghani, M., Sagae, K., Sachdeva, S., & Gratch, J. (2014). Analyzing political rhetoric in conservative and liberal weblogs related to the construction of the “Ground Zero Mosque.”. Journal of Information Technology & Politics, 11, 1–14. doi:10.1080/19331681.2013.826613
14. Duggan, M., Ellison, N. B., Lampe, C., Lenhart, A., & Madden, M. (2015). Social media update 2014. Washington: Pew Research Center.
15. Eichstaedt, J. C., Schwartz, H. A., Kern, M. L., Park, G., Labarthe, D. R., Merchant, R. M.,.. Seligman, M. E. P. (2015). Psychological language on Twitter predicts county-level heart disease mortality. Psychological Science, 26, 159–169. doi:10.1177/0956797614557867
16. Frank, E., Hall, M., Reutemann, P., & Trigg, L. (2006). Weka 3: Data mining software in Java. Hamilton: University of Waikato.
17. Frimer, J. A., Aquino, K., Gebauer, J. E., Zhu, L. L., & Oakes, H. (2015). A decline in prosocial language helps explain public disapproval of the US Congress. Proceedings of the National Academy of Sciences, 112, 6591–6594.
18. Graham, J., Haidt, J., & Nosek, B. A. (2009). Liberals and conservatives rely on different sets of moral foundations. Journal of Personality and Social Psychology, 96, 1029–1046. doi:10.1037/a0015141
19. Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences, 101(Suppl. 1), 5228–5235.
20. Han, B., & Baldwin, T. (2011). Lexical normalisation of short text messages: Makn sens a# twitter. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (Vol. 1, pp. 368–378). Stroudsburg, PA, USA: Association for Computational Linguistics.
21. Hart, R. P. (1984). Verbal style and the presidency: A computer-based analysis. New York: Academic Press.
22. Hirschberg, J., & Manning, C. D. (2015). Advances in natural language processing. Science, 349, 261–266. doi:10.1126/science.aaa8685
23. Corp, I. B. M. (2011). SPSS Modeler 16 algorithms guide. Armonk: Author.
24. Ireland, M. E., Slatcher, R. B., Eastwick, P. W., Scissors, L. E., Finkel, E. J., & Pennebaker, J. W. (2011). Language style matching predicts relationship initiation and stability. Psychological Science, 22, 39–44.
25. Johnson, S. C. (1967). Hierarchical clustering schemes. Psychometrika, 32, 241–254.
26. Klimt, B., & Yang, Y. (2004). The Enron Corpus: A new dataset for email classification research. In Machine learning: ECML 2004 (pp. 217–226). Berlin, Germany: Springer.
27. Kyle, K., & Crossley, S. A. (2015). Automatically assessing lexical sophistication: Indices, tools, findings, and application. TESOL Quarterly, 49, 757–786.
28. Le, Q. V., & Mikolov, T. (2014). Distributed representations of sentences and documents. arXiv [cs.CL]. Retrieved from http://arxiv.org/abs/1405.4053
29. Leech, G., Garside, R., & Atwell, E. S. (1983). The automatic grammatical tagging of the LOB Corpus. International Computer Archive of Modern and Medieval English Journal, 7, 13–33.
30. Lewis, D. D. (1998). Naive (Bayes) at forty: The independence assumption in information retrieval. In Machine learning: ECML-98 (Lecture Notes in Computer Science, Vol. 1398, pp. 4–15). New York, NY: Springer. doi:10.1007/BFb0026666
31. Lu, X. (2010). Automatic analysis of syntactic complexity in second language writing. International Journal of Corpus Linguistics, 15, 474–496. doi:10.1075/ijcl.15.4.02lu
32. MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In L. M. Le Cam & J. Neyman (Eds.), Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability Vol 1 (pp. 281–297). Berkeley: University of California Press.
33. Mander, J. (2015). Global Web Index Social Summary Q1 2015. London: Global Web Index.
34. Manning, C. D., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S. J., & McClosky, D. (2014). The Stanford CoreNLP natural language processing toolkit. In Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations (pp. 55–60). Stroudsburg, PA, USA: Association for Computational Linguistics.
35. Math, C. (2014). The Apache Commons Mathematics Library. Retrieved from commons.apache.org/proper/commons-math/, September 8, 2013.
36. McCallum, A. K. (2002). MALLET: A machine learning for language toolkit [Software]. Retrieved from http://mallet.cs.umass.edu
37. McNamara, D. S., Graesser, A. C., McCarthy, P. M., & Cai, Z. (2014). Automated evaluation of text and discourse with Coh-Metrix. Cambridge: Cambridge University Press.
38. Mehl, M. R., Gosling, S. D., & Pennebaker, J. W. (2006). Personality in its natural habitat: Manifestations and implicit folk theories of personality in daily life. Journal of Personality and Social Psychology, 90, 862–877. doi:10.1037/0022-3514.90.5.862
39. Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., Leisch, F., Chang, C.-C., & Lin, C.-C. (2015). Package “e1071.” Retrieved from https://cran.r-project.org/web/packages/e1071/index.html
40. Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, & K. Q. Weinberger (Eds.), Advances in neural information processing systems 26 (pp. 3111–3119). Cambridge: MIT Press.
41. Mukherjee, A., & Liu, B. (2010). Improving gender classification of blog authors. In Proceedings of the 2010 conference on Empirical Methods in natural Language Processing (pp. 207–217). Stroudsburg, PA, USA: Association for Computational Linguistics.
42. Newman, M. L., Pennebaker, J. W., Berry, D. S., & Richards, J. M. (2003). Lying words: Predicting deception from linguistic styles. Personality and Social Psychology Bulletin, 29, 665–675.
43. Nguyen, M.-T., & Lim, E.-P. (2014). On predicting religion labels in microblogging networks. In Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1211–1214). New York, NY, USA: ACM.
44. Papadimitriou, C. H., Tamaki, H., Raghavan, P., & Vempala, S. (1998). Latent semantic indexing: A probabilistic analysis. In Proceedings of the Seventeenth ACM SIGACT–SIGMOD–SIGART Symposium on Principles of Database Systems (pp. 159–168). New York, NY, USA: ACM.
45. Pennebaker, J. W., Booth, R. J., & Francis, M. E. (2007). Linguistic inquiry and word count: LIWC [Computer software]. Austin: Liwc.net.
46. Pennebaker, J. W., Francis, M. E., & Booth, R. J. (2001). Linguistic inquiry and word count: LIWC 2001. Mahwah: Erlbaum.
47. Pennebaker, J. W., & King, L. A. (1999). Linguistic styles: Language use as an individual difference. Journal of Personality and Social Psychology, 77, 1296–1312. doi:10.1037/0022-3514.77.6.1296
48. Pennington, J., Socher, R., & Manning, C. D. (2014). Glove: Global vectors for word representation. Proceedings of the Empirical Methods in Natural Language Processing Conference (EMNLP 2014), 12, 1532–1543.
49. Porter, M. F. (2001). Snowball: A language for stemming algorithms. snowball.tartarus.org. Retrieved from http://snowball.tartarus.org/texts/introduction.html
50. Porter, M. F. (2006). An algorithm for suffix stripping. Program: Electronic Library and Information Systems, 40, 211–218.
51. Rude, S., Gortner, E.-M., & Pennebaker, J. (2004). Language use of depressed and depression-vulnerable college students. Cognition and Emotion, 18, 1121–1133.
52. Stirman, S. W., & Pennebaker, J. W. (2001). Word use in the poetry of suicidal and nonsuicidal poets. Psychosomatic Medicine, 63, 517–522.
53. Stone, P. J., Dunphy, D. C., & Smith, M. S. (1966). The general inquirer: A computer approach to content analysis. Cambridge: MIT Press.
54. Tausczik, Y. R., & Pennebaker, J. W. (2010). The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology, 29, 24–54.
55. T. M. I. Project. (2015). How millennials get news: Inside the habits of America’s first digital generation. Retrieved November 8, 2015, from www.mediainsight.org/PDFs/Millennials/Millennials%20Report%20FINAL.pdf
56. Van Mieghem, P. (2011). Human psychology of common appraisal: The Reddit score. IEEE Transactions on Multimedia, 13, 1404–1406.
57. Wojcik, S. P., Hovasapian, A., Graham, J., Motyl, M., & Ditto, P. H. (2015). Conservatives report, but liberals display, greater happiness. Science, 347, 1243–1246. doi:10.1126/science.1260817
58. Yang, H., Zhuang, T., & Zong, C. (2015). Domain adaptation for syntactic and semantic dependency parsing using deep belief networks. Transactions of the Association for Computational Linguistics, 3, 271–282.
59. Yu, B., Kaufmann, S., & Diermeier, D. (2008). Classifying party affiliation from political speech. Journal of Information Technology and Politics, 5, 33–48.