1. 1. The pen is mightier than the sword, Wikipedia, 22-Nov2016. [Online]. Avail- able: https://en.wikipedia.org/w/index.php?title=The_pen_is_mig htier_than_ the_sword{&}oldid=750939396. (Accessed 2 December 2016). 
   2. B. Liu, Handbook Chapter: Sentiment Analysis and Subjectivity. Handbook of Natural Language Processing, Handbook of Natural Language Processing, Marcel Dekker, Inc. New York, NY, USA, 2009.
   3. K. Dave, S. Lawrence, D.M. Pennock, Mining the peanut gallery: Opinion extraction and semantic classification of product reviews, in Proceedings of the 12th international conference on World Wide Web, 2003, pp. 519–528.
   4. J.A. Richmond, Spies in ancient Greece, in: Greece and Rome (Second Series), vol. 45, no. 01, 1998, pp. 1–18.
   5. J. Thorley, Athenian Democracy, Psychology Press, 2004.
   6. D.D. Droba, Methods used for measuring public opinion, Am. J. Sociol. (1931) 410–423.
   7. Public opinion quarterly. [Online]. Available: http://poq.oxfordjournals.org. (Accessed 2 December 2016).
   8. R. Stagner, The cross-out technique as a method in public opinion analysis, J. Soc. Psychol. 11 (1) (1940) 79– 90.
   9. A.L. Knutson, Japanese opinion surveys: the special need and the special difficulties, Publ. Opin. Q. 9 (3) (1945) 313– 319.
   10. A.K. Nassirtoussi, S. Aghabozorgi, T.Y. Wah, D.C.L. Ngo, Text mining for market prediction: A systematic review, Expert Syst. Appl. 41 (16) (2014) 7653–7670.
   11. P. Burnap, et al., Tweeting the terror: modelling the social media reaction to the Woolwich terrorist attack, Soc. Netw. Anal. Min. 4 (1) (2014) 1–14.
   12. A. Reyes, P. Rosso, On the difficulty of automatically detecting irony: beyond a simple case of negation, Knowl. Inf. Syst. 40 (3) (2014) 595–614.
   13. A. Hogenboom, B. Heerschop, F. Frasincar, U. Kaymak, F. de Jong, Multi-lingual support for lexicon-based sentiment analysis guided by semantics, Decis. Support Syst. 62 (2014) 43–53.
   14. M.D. Munezero, C.S. Montero, E. Sutinen, J. Pajunen, Are they different? Affect, feeling, emotion, sentiment, and opinion detection in text, IEEE Trans. Affective Comput. 5 (2) (2014) 101–111.
   15. E. Cambria, P. Gastaldo, F. Bisio, R. Zunino, An ELMbased model for affective analogical reasoning, Neurocomputing 149 (2015) 443–455.
   16. V. Garousi, M.V. Mäntylä, Citations research topics and active countries in software engineering: A bibliometrics study, Comput. Sci. Rev. 19 (2016) 56– 77.
   17. H. Du, B. Li, M.A. Brown, G. Mao, R. Rameezdeen, H. Chen, Expanding and shifting trends in carbon market research: a quantitative bibliometric study, J. Cleaner Prod. 103 (2015) 104–111.
   18. M.P. Ferreira, J.C. Santos, M.I.R. de Almeida, N.R. Reis, Mergers & acquisitions research: A bibliometric study of top strategy and international business journals 1980– 2010, J. Bus. Res 67 (12) (2014) 2550–2558.
   19. M. Kozak, L. Bornmann, L. Leydesdorff, How have the eastern european coun- tries of the former warsaw pact developed since 1990? A bibliometric study, Scientometrics 102 (2) (2015) 1101–1117.
   20. T.L. Griffiths, M. Steyvers, Finding scientific topics, Proc. Natl. Acad. Sci. 101 (Suppl. 1) (2004) 5228–5235