REFERENCES
- Alexopoulos, M. (2011). Read all about it!! What happens following a technology shock? American Economic Review, 101, 1144–1179. Retrieved from http://ideas.repec.org/a/aea/aecrev/v101y2011i4p1144-79.html
- Alexopoulos, M., & Cohen, J. (2008). Uncertainty and the credit crisis: The worst might be over. VoxEU. Retrieved from http://www.voxeu.org/article/uncertainty-and-credit-crisis-worst-may-be-over
- Alexopoulos, M., & Cohen, J. (2009a). Measuring our ignorance, one book at a time: New indicators of technical change, 1909–1949. Journal of Monetary Economics, 56, 450–470. Retrieved from http://ideas.repec.org/a/eee/moneco/v56y2009i4p450-470.html
- Alexopoulos, M., & Cohen, J. (2009b). Uncertain times, uncertain measures. University of Toronto Working Paper #352. Retrieved from http://ideas.repec.org/p/tor/tecipa/tecipa-352.html
- Alexopoulos, M., & Cohen J. (in press). The power of print: Uncertainty shocks, markets, and the economy. International Review of Economics & Finance.
- Alexopoulos, M., & Tombe, T. (2012). Management matters. Journal of Monetary Economics, 59, 269–285. Retrieved from http://ideas.repec.org/a/eee/moneco/v59y2012i3p269-285.html
- Anawis, M.A. (2014, January 6). Text mining: The next data frontier [Web log post]. . Retrieved from http://www.scientificcomputing.com/blogs/2014/01/text-mining-next-data-frontier
- Archak, N., Ghose, A., & Ipeirotis, P.G. (2011). Deriving the pricing power of product features by mining consumer reviews. Management Science, 57(8), 1485–1509.
- Baker, S., Bloom, N., & Davis, S.J. (2013, June 13). Measuring economic policy uncertainty. Stanford mimeo. . Retrieved from http://web.stanford.edu/group/sssl/cgi-bin/wordpress/wp-content/uploads/2013/12/BakerBloomDavis_PolicyUncertainty_June13.pdf
- Bergman, C.M., Hunter, L.E., & Rzhetsky, A. (2013, April 17). Announcing the PLOS Mining Collection. [Web log post]. . Retrieved from http://blogs.plos.org/everyone/2013/04/17/announcing-the-plos-text-mining-collection/
- Berry, M.J., & Linoff, G.S. (2004). Data mining techniques: For marketing, sales, and customer relationship management. Indianapolis, IN: John Wiley.
- Bhattacharya, J., & Packalen, M. (2011). Opportunities and benefits as determinants of the direction of scientific research. Journal of Health Economics, 30, 603–615.
- Billington, J. (2013). CCC's Text and data mining pilot service: Overview of 2013 pilot program [Powerpoint slides]. Retrieved from http://www.stm-assoc.org/2013_05_20_FACT2_Billington_CCCs_Text_and_Data_Mining_Pilot_Service.pdf
- Bollen, J., Mao, H., & Zeng, X.-J. (2010). Twitter mood predicts the stock market. Journal of Computational Science, 2, 1–8.
- Clark, J. (2013). Text mining and scholarly publishing. Amsterdam, The Netherlands: Publishing Research Consortium. Retrieved from http://www.publishingresearch.org.uk/documents/PRCTextMiningandScholarlyPublishinFeb2013.pdf
- Francesconi, E., Montemagni, S., Peters, W., & Tiscornia, D. (Eds.). (2010). Semantic processing of legal texts: Where the language of law meets the law of language (Vol. 40). Berlin, Germany: Springer.
- Hendry, S. (2012). Central Bank communication or the media's interpretation: What moves markets? Bank of Canada working paper, 2012-9. Retrieved from http://hdl.handle.net/10419/80758
- Hendry, S., & Madeley, A. (2010). Text mining and the information content of Bank of Canada communications. Bank of Canada working paper, 2010-31. Retrieved from http://www.bankofcanada.ca/wp-content/uploads/2010/11/wp10-31.pdf
- Heneghan, M., Hazan, C., Halpern, A., & Oliveria, S. (2007). Skin cancer coverage in a national newspaper: A teachable moment. Journal of Cancer Education, 22, 99–104.
- Hu, M., & Liu, B. (2004, July). Mining opinion features in customer reviews. AAAI, 4, 7555–760.
- International Federation of Library Associations and Institutions. (2013). IFLA statement on text and data mining. Retrieved from http://www.ifla.org/publications/ifla-statement-on-text-and-data-mining-2013
- Lancashire, I., & Hirst, G. (2009, March). Vocabulary changes in Agatha Christie's mysteries as an indication of dementia: A case study. In 19th Annual Rotman Research Institute Conference, Cognitive Aging: Research and Practice, 8–10. . Retrieved from ftp://ftp.cs.toronto.edu/pub/gh/Lancashire+Hirst-extabs-2009.pdf
- Lavengood, K.A., & Kiser, P. (2007, May). Information professionals in the text mine. Online, 31(3). . Retrieved from http://www.infotoday.com/online/may07/Lavengood_Kiser.shtml
- Le, X., Lancashire, I., Hirst, G., & Jokel, R. (2011). Longitudinal detection of dementia through lexical and syntactic changes in writing: a case study of three British novelists. Literary and Linguistic Computing, 26(4), 435–461.
- Lee, K., Agrawal, A., & Choudhary, A. (2013, August). Real-time disease surveillance using Twitter data: Demonstration on flu and cancer. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge Discovery and Data Mining (pp. 1474–1477). New York, NY: ACM. . Retrieved from http://users.eecs.northwestern.edu/∼kml649/publication/kdd2013.pdf
- Loughran, T., & McDonald, B. (2011). When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. Journal of Finance, 66, 35–65.
- Loughran, T., & McDonald, B. (2013). IPO first-day returns, offer price revisions, volatility, and form S-1 language. Journal of Financial Economics, 109, 307–326.
- Lucca, D., & Trebbi, F. (2009). Measuring central bank communication: An automated approach with application to FOMC statements. NBER Working Paper, No. 15367. Retrieved from http://www.nber.org/papers/w15367.pdf
- McDonald, D., & Kelly, U. (2012). The value and benefits of text mining. JISC. Retrieved from http://www.jisc.ac.uk/reports/value-and-benefits-of-text-mining
- Michel, J.B., Shen, Y.K., Aiden, A.P., Veres, A., Gray, M.K., The Google Books Team, … Aiden, E.L. (2011). Quantitative analysis of culture using millions of digitized books. Science, 331(6014), 176–182.
- O’Connor, B., Balasubramanyan, R., Routledge, B.R., & Smith, N.A. (2010). From tweets to polls: Linking text sentiment to public opinion time series. ICWSM, 11, 122–129.
- On, P. (2013). Top 30 software for text analysis, text mining, text analytics. Predictive Analytics Today. Retrieved from http://www.predictiveanalyticstoday.com/top-30-software-for-text-analysis-text-mining-text-analytics/
- Packalen, M., & Bhattacharya, J. (2012). Words in patents: Research inputs and the value of innovativeness in invention. NBER Working Paper, No. 18494. Retrieved from http://www.nber.org/papers/w18494.pdf
- Perc, M. (2012, December). Evolution of the most common English words and phrases over the centuries. Journal of the Royal Society Interface, 7, 3323–3328.
- Reilly, B.F. (2012). CRL reports: When machines do research, part 2: Text-mining and libraries. The Charleston Advisor, 14(2), 75–76. Retrieved from http://charleston.publisher.ingentaconnect.com/content/charleston/chadv/2012/00000014/00000002/art00022
- Shen, C. (2013, January/February). Big data, analytics and elections. Analytics-Magazine.Org, 40–44. . Retrieved from http://www.analytics-magazine.org/january-february-2013/731-big-data-analytics-and-elections
- Smit, E., & Van der Graaf, M. (2011). Journal article mining: A research study into practices, policies, plans and promises. Amsterdam, The Netherlands: Publishing Research Consortium, Amsterdam. Retrieved from http://www.publishingresearch.org.uk/documents/PRCSmitJAMreport2.30June13.pdf
- Smit, E., & Van der Graaf, M. (2012). Journal article mining: The scholarly publishers’ perspective. Learned Publishing, 25(1), 35–46.
- STM International Association of Scientific Technical & Medical Publishers. (2013, November). Text and data mining for non-commercial scientific research: A statement of commitment by STM publishers to a roadmap to enable text and data mining (TDM) for non commercial scientific research in the European Union. . Retrieved from http://www.stm-assoc.org/2013_11_11_Text_and_Data_Mining_Declaration.pdf
- Tumasjan, A., Sprenger, T.O., Sandner, P.G., & Welpe, I.M. (2010). Predicting elections with Twitter: What 140 characters reveal about political sentiment. ICWSM, 10, 178–185.
- Wyner, A., Mochales-Palau, R., Moens, M.F., & Milward, D. (2010). Approaches to text mining arguments from legal cases. Berlin, Germany: Springer.