References Allen, D.E. and M. McAleer (2018a) Fake news and indiference to scientifie fact: President Trump's confused tweets on global warming, climate change and weather, Scientometrics, 117(1), 625-629. Allen, D.E. and M. McAleer (2018b) President Trump Tweets Supreme Leader Kim Jong-Un on Nuclear Weapons: A Comparison with Climate Change, Sustainability, 10(7,2310), 1-6. Allen, D.E., M. McAleer, and D. M. Reid (2018) Fake News and Indifference to Truth: Dissecting Tweets and State of the Union Addresses by Presidents Obama and Trump, Advances in Decision Sciences, 22(A) 1-23. http://journal.asia.edu.tw/ADS/category/table-of-contents-for-year2018/, ISSN 2090-3367 (Online). Allen, D.E., M. McAleer, and A.K. Singh (2015), Machine news and volatility: The Dow Jones Industrial Average and the TRNA real-time high frequency Sentiment Series, chapter 19 in Handbook of High Frequency Trading, Ed. G.N. Gregoriou, Elsevier, Academic Press. Allen, D.E., M. McAleer and A.K. Singh (2017), An entropy-based analysis of the relationship between the DOW JONES Index and the TRNA Sentiment series, Applied Economics, 49, 677-692. Allen, D.E., M. McAleer and A.K. Singh (2018) Daily market news sentiment and stock prices, Applied Economics, forthcoming, https://doi.org/10.1080/00036846.2018.1564115. Barber, B.M., and T. Odean (2008), All that glitters: The efect of attention and news on the buying behaviour of individual and institutional investors, Review of Financial Studies, 21(2), 785818. Cahan R., J. Jussa and Y. Luo (2009), Breaking news: How to use news sentiment to pick stocks, MacQuarie US Research Report. Da, Z.H.I., J. Engelberg, and P. Gao (2011), In search of attention, Journal of Finance, 66 (5), 1461-1499. diBartolomeo, D., and S. Warrick (2005), Making covariance based portfolio risk models sensitive to the rate at which markets react to new information. In J. Knight and S. Satchell. (Eds.), Linear Factor Models, Elsevier Finance. Dzielinski, M., M.O. Rieger, and T. Talpsepp (2011), Volatility asymmetry, news, and private investors, The Handbook of News Analytics in Finance (pp. 255-270), Wiley. Fellows, I. (2018) wordcloud, https://CRAN.R-project.org/package=wordcloud. Feinerer, I. and K. Hornik (2018) tm: Text Mining Package. R package version 0.7-6. https://CRAN.R-project.org/package=tm. Hafez, P. and J. Xie (2012), Factoring Sentiment Risk into Quant Models, RavenPack International S.L. Jockers, M.L. (2015) Syuzhet: Extract Sentiment and Plot Arcs from Text, https://github.com/mjockers/syuzhet. Mehrabian, A. (1981), Silent Messages: Implicit Communication of Emotions and Attitudes, Belmont, CA, Wadsworth. Mitra, L., G. Mitra, and D. diBartolomeo (2009), Equity portfolio risk (volatility) estimation using market information and sentiment, Quantitative Finance, 9(8), 887-895. Pröllochs, N., S. Fuerriegel and D. Neumann (2017), Understanding negations in information processing: Learning from replicating human behaviour, Working Paper, Information Systems Research, University of Freiburg, available at SSRN https://ssrn.com/abstract=2954460. Propp, V. (1928/1968) Morphology of the Folk Tale, English trans. Laurence Scott. TX: University of Texas Press (first published in Moscow in 1928; English, 1968). Shklovsky, V. (1917/1965). Art as Technique, in L T Lemon and M Reis, eds., (1965) Russian Formalist Criticism, University of Nebraska Press. Smith, A.D. (1994) Gastronomy or geology? The role of nationalism in the reconstruction of nations, Nations and Nationalism, 1(1) 3-23. Swartz, T. (1973), The Responsive Chord, New York, Anchor Press/Doubleday. Tetlock, P.C. (2007), Giving content to investor sentiment: The role of media in the stock market, Journal of Finance, 62, 11391167. Tetlock, P.C., S.A. Macskassy, and M. Saar-Tsechansky (2008), More than words: Quantifying language to measure rms' fundamentals, Journal of Finance, 63, 14271467.