Analyzing public sentiments online: combining human- and computer-based content analysis

References

• Annett, M., & Kondrak, G. (2008). A comparison of sentiment analysis techniques: Polarizing movie blogs. In S. Bergler (Ed.), Advances in artificial intelligence (Vol. 5032, pp. 25–35). Berlin Heidelberg: Springer.
   Google Scholar
• Babbie, E. (1998). The practice of social research (8th ed.). Belmont, CA: Wadsworth.
   Google Scholar
• Berelson, B. (1952). Content analysis in communication research. New York, NY: Free Press.
   Google Scholar
• Binder, A. R. (2012). Figuring out #Fukushima: An initial look at functions and content of US twitter commentary about nuclear risk. Environmental Communication, 6(2), 268–277. doi:10.1080/17524032.2012.672442
   Google Scholar
• Boiy, E., & Moens, M.-F. (2009). A machine learning approach to sentiment analysis in multilingual web texts. Information Retrieval, 12(5), 526–558. doi:10.1007/s10791-008-9070-z
   Web of Science ®Google Scholar
• Bollier, D., & Firestone, C. M. (2010). The promise and peril of big data. Washington, DC: Aspen Institute, Communications and Society Program.
   Google Scholar
• Bolsen, T., & Cook, F. L. (2008). The polls-trends – Public opinion on energy policy: 1974–2006. Public Opinion Quarterly, 72(2), 364–388. doi: 10.1093/poq/nfn019
   Web of Science ®Google Scholar
• boyd, d., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662–679. doi:10.1080/1369118X.2012.678878
   Web of Science ®Google Scholar
• Cacciatore, M. A., Anderson, A. A., Choi, D.-H., Brossard, D., Scheufele, D. A., Liang, X., … Dudo, A. (2012). Coverage of emerging technologies: A comparison between print and online media. New Media & Society, 14(6), 1039–1059. doi:10.1177/1461444812439061
   Web of Science ®Google Scholar
• Ceron, A., Curini, L., Iacus, S. M., & Porro, G. (2014). Every tweet counts? How sentiment analysis of social media can improve our knowledge of citizens’ political preferences with an application to Italy and France. New Media & Society, 16(2), 340–358. doi:10.1177/1461444813480466
   Web of Science ®Google Scholar
• Chamlertwat, W., Bhattarakosol, P., & Rungkasiri, T. (2012). Discovering consumer insight from Twitter via sentiment analysis. Journal of Universal Computer Science, 18(8), 973–992.
   Web of Science ®Google Scholar
• Chow-White, P. A., & Green, S. E. G. (2013). Data mining difference in the age of big data: Communication and the social shaping of genome technologies from 1998 to 2007. International Journal of Communication, 7, 556–583.
   Web of Science ®Google Scholar
• Christensen, C. (2011). Twitter revolutions? Addressing social media and dissent. The Communication Review, 14(3), 155–157. doi:10.1080/10714421.2011.597235
   Google Scholar
• Conway, M. (2006). The subjective precision of computers: A methodological comparison with human coding in content analysis. Journalism & Mass Communication Quarterly, 83(1), 186–200. doi: 10.1177/107769900608300112
   Web of Science ®Google Scholar
• Danowski, J. A. (1993). Network analysis of message content. In W. D. Richards & G. A. Barnett (Eds.), Progress in communication sciences (Vol. 12, pp. 197–221). Norwood, NJ: Ablex.
   Google Scholar
• Dillow, C. (2010). Turning retired military jets into next-gen nano-satellite launchers. Retrieved from http://www.popsci.com/technology/article/2010-10/nanolauncher-wants-turn-retired-military-jets-next-gen-satellite-launchers
   Google Scholar
• Doerfel, M. L., & Barnett, G. A. (1996). CATPACT for text analysis. In K. Krippendorff & M. A. Bock (Eds.), The content analysis reader (pp. 416–420). Thousand Oaks, CA: Sage.
   Google Scholar
• Doerfel, M. L., & Barnett, G. A. (1999). A semantic network analysis of the international communication association. Human Communication Research, 25(4), 589–603. doi: 10.1111/j.1468-2958.1999.tb00463.x
   Web of Science ®Google Scholar
• Friedman, S. M. (2011). Three Mile Island, Chernobyl, and Fukushima: An analysis of traditional and new media coverage of nuclear accidents and radiation. Bulletin of the Atomic Scientists, 67(5), 55–65. doi:10.1177/0096340211421587
   Web of Science ®Google Scholar
• Gundecha, P., & Liu, H. (2012). Mining social media: A brief introduction. Tutorials in Operations Research, 1(4), 1–17.
   Google Scholar
• Himelboim, I., McCreery, S., & Smith, M. (2013). Birds of a feather tweet together: Integrating network and content analyses to examine cross-ideology exposure on Twitter. Journal of Computer-mediated Communication, 18(2), 40–60. doi:10.1111/jcc4.12001
   Web of Science ®Google Scholar
• Holsti, O. R. (1969). Content analysis for the social sciences and humanities. Reading, MA: Addison-Wesley.
   Google Scholar
• Hopkins, D. J., & King, G. (2010). A method of automated nonparametric content analysis for social science. American Journal of Political Science, 54(1), 229–247. doi: 10.1111/j.1540-5907.2009.00428.x
   Web of Science ®Google Scholar
• Illia, L., Sonpar, K., & Bauer, M. W. (2014). Applying co-occurrence text analysis with ALCESTE to studies of impression management. British Journal of Management, 25(2), 352–372. doi:10.1111/j.1467-8551.2012.00842.x
   Web of Science ®Google Scholar
• Jenkins-Smith, H. (2011). Public beliefs, concerns and preferences regarding the management of used nuclear fuel and high level radioactive waste. Washington DC: US Energy Department Blue Ribbon 8 Commission on America’s Nuclear Future. Retrieved from https://curie.ornl.gov/system/files/documents/not%20yet%20assigned/hank_jenkins-smith_brc_paper_final.pdf.
   Google Scholar
• Karpf, D. (2012). Social science research methods in Internet time. Information, Communication & Society, 15(5), 639–661. doi: 10.1080/1369118X.2012.665468
   Web of Science ®Google Scholar
• Kinsella, W. J. (2012). Environments, risks, and the limits of representation: Examples from nuclear energy and some implications of Fukushima. Environmental Communication: A Journal of Nature and Culture, 6(2), 251–259. doi:10.1080/17524032.2012.672928
   Web of Science ®Google Scholar
• Krippendorff, K. (2004). Reliability in content analysis – Some common misconceptions and recommendations. Human Communication Research, 30(3), 411–433.
   Web of Science ®Google Scholar
• Krippendorff, K. (2012). Content analysis: An introduction to its methodology (3rd ed.). Thousand Oaks, CA: Sage.
   Google Scholar
• Lewis, S. C., Zamith, R., & Hermida, A. (2013). Content analysis in an era of big data: A hybrid approach to computational and manual methods. Journal of Broadcasting & Electronic Media, 57(1), 34–52. doi:10.1080/08838151.2012.761702
   Web of Science ®Google Scholar
• Li, N., Heather, A., Su, L. Y.-F., Xenos, M. A., Scheufele, D. A., & Brossard, D. (2013). Use Twitter to assess public opinion pre- and post-Fukushima. Paper presented at the International Communication Association, London, UK.
   Google Scholar
• Liu, B., & Zhang, L. (2012). A survey of opinion mining and sentiment analysis. In C. C. Aggarwal & C. Zhai (Eds.), Mining text data (pp. 415–463). New York: Springer.
   Google Scholar
• Matthes, J., & Kohring, M. (2008). The content analysis of media frames: Toward improving reliability and validity. Journal of Communication, 58(2), 258–279. doi:10.1111/j.1460-2466.2008.00384.x
   Web of Science ®Google Scholar
• Miller, M. M. (1997). Frame mapping and analysis of news coverage of contentious issues. Social Science Computer Review, 15(4), 367–378. doi:10.1177/089443939701500403
   Web of Science ®Google Scholar
• Morris, R. (1994). Computerized content analysis in management research: A demonstration of advantages & limitations. Journal of Management, 20(4), 903–931. doi:10.1177/014920639402000410
   Web of Science ®Google Scholar
• Morstatter, F., Pfeffer, J., Liu, H., & Carley, K. M. (2013). Is the sample good enough? Comparing data from Twitter’s streaming API with Twitter’s firehose. In Proceedings of International Conference on Weblogs and Social Media, Cambridge, MA: AAAI Press.
   Google Scholar
• Murthy, D. (2011). Twitter: Microphone for the masses? Media, Culture & Society, 33(5), 779–789. doi:10.1177/0163443711404744
   Web of Science ®Google Scholar
• Nacos, B. L., Shapiro, R. Y., Young, J. T., Fan, D. P., Kjellstrand, T., & McCaa, C. (2009). Comparing human coding and a computer-assisted method. In K. Krippendorff & M. A. Bock (Eds.), The content analysis reader (pp. 243–252). Thousand Oaks, CA: Sage.
   Google Scholar
• Nasukawa, T., & Yi, J. (2003). Sentiment analysis: Capturing favorability using natural language processing. Paper presented at the Proceedings of the 2nd international conference on Knowledge capture, Sanibel Island, Florida, USA.
   Google Scholar
• Neuendorf, K. A. (2002). The content analysis guidebook. Thousand Oaks, CA: Sage.
   Google Scholar
• Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1–135. doi: 10.1561/1500000011
   Google Scholar
• Pew Research Center. (2011). Opposition to nuclear power rises amid Japanese crisis. Pew Research Center for the People & the Press, Washington, DC.
   Google Scholar
• Pew Research Center. (2013). Twitter reaction to events often at odds with overall public opinion. Retrieved from http://www.pewresearch.org/2013/03/04/twitter-reaction-to-events-often-at-odds-with-overall-public-opinion/
   Google Scholar
• Porter, A. L., Youtie, J., Shapira, P., & Schoeneck, D. J. (2008). Refining search terms for nanotechnology. Journal of Nanoparticle Research, 10, 715–728. doi:10.1007/s11051-007-9266-y
   Web of Science ®Google Scholar
• Potter, W. J. (2006). Establishing validity in content analyses of risky health behaviors portrayed in media messages the Annenberg Health Media Coding Project. Philadelphia: University of Pennsylvania.
   Google Scholar
• Riffe, D., Lacy, S., & Fico, F. G. (2005). Analyzing media messages: Using quantitative analysis in research. Mahwah, NJ: Lawrence Erlbaum Associates.
   Google Scholar
• Runge, K. K., Yeo, S. K., Cacciatore, M. A., Scheufele, D. A., Brossard, D., Xenos, M. A., … Su, L. Y.-F. (2013). Tweeting nano: How public discourses about nanotechnology develop in social media environments. Journal of Nanoparticle Research, 15(1381). doi:10.1007/s11051-012-1381-8
   Web of Science ®Google Scholar
• Shapiro, G. (2009). The future of coders. In K. Krippendorff & M. A. Bock (Eds.), The content analysis reader (pp. 234–242). Thousand Oaks, CA: Sage.
   Google Scholar
• Short, J. C., Broberg, J. C., Cogliser, C. C., & Brigham, K. H. (2010). Construct validation using computer-aided text analysis (CATA): An illustration using entrepreneurial orientation. Organizational Research Methods, 13(2), 320–347. doi: 10.1177/1094428109335949
   Web of Science ®Google Scholar
• Simon, A. F., & Xenos, M. (2004). Dimensional reduction of world-frequency data as a substitute for intersubjective content analysis. Political Analysis, 12(1), 63–75. doi:10.1093/pan/mph004
   Web of Science ®Google Scholar
• Sjøvaag, H., & Stavelin, E. (2012). Web media and the quantitative content analysis: Methodological challenges in measuring online news content. Convergence: The International Journal of Research into New Media Technologies. doi:10.1177/1354856511429641
   Web of Science ®Google Scholar
• Smith, M. (2011). Japan Nuclear Disaster Tops Scale. Retrieved from http://www.cnn.com/2011/WORLD/asiapcf/04/12/japan.nuclear.reactors/
   Google Scholar
• Su, L. Y.-F., Cacciatore, M. A., Scheufele, D. A., Brossard, D., & Xenos, M. A. (2014). Inequalities in scientific understanding: Differentiating between factual and perceived knowledge gaps. Science Communication, 36(3), 352–378. doi:10.1177/1075547014529093
   Web of Science ®Google Scholar
• Wang, H., Can, D., Kazemzadeh, A., Bar, F., & Narayanan, S. (2012). A system for real-time Twitter sentiment analysis of 2012 U.S. presidential election cycle. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, Jeju, Republic of Korea.
   Google Scholar
• Woelfel, J., & Fink, E. L. (1980). The measurement of communication processes: GALILEO theory and method. New York, NY: Academic Press.
   Google Scholar
• Yi, J., Nasukawa, T., Bunescu, R., & Niblack, W. (2003). Sentiment analyzer: Extracting sentiments about a given topic using natural language processing techniques. In Proceedings of the IEEE International Conference on Data Mining, Melbourne, Florida, USA.
   Google Scholar
• Züll, C., & Landmann, J. (2004). Computer-assisted content analysis without dictionary. Paper presented at the Sixth International Conference on Logic and Methodology (RC33) – Recent Developments and Applications in Social Research Methodology, Amsterdam, the Netherlands.
   Google Scholar