Many techniques from traditional survey methodology

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Many techniques from traditional survey methodology can also be used again for automatic opinion measurement. For example, polls routinely use stratified sampling and weighted designs to ask questions of a representative sam- ple of the population. Given that many social media sites include user demographic information, such a design is a sensible next step. Eventually, we see this research progressing to align with the more general goal of query-driven sentiment analysis where one can ask more varied questions of what people are thinking based on text they are already writing. Modeling traditional survey data is a useful application of sentiment analysis. But it is also a stepping stone toward larger and more sophisticated applications. Acknowledgments This work is supported by the Center for Applied Research in Technology at the Tepper School of Business, and the Berkman Faculty Development Fund at Carnegie Mellon University. We would like to thank the reviewers for help- ful suggestions, Charles Franklin for advice in interpreting election polling data, and Brendan Meeder for contribution of the Twitter scrape. References Antweiler, W., and Frank, M. Z. 2004. Is all that talk just noise? the information content of internet stock message boards. Journal of Finance 59(3):1259–1294. Chang, L. C., and Krosnick, J. A. 2003. National surveys via RDD telephone interviewing vs. the internet: Compar- ing sample representativeness and response quality. Unpub- lished manuscript. Das, S. R., and Chen, M. Y. 2007. Yahoo! for Amazon: Sen- timent extraction from small talk on the web. Management Science 53(9):1375–1388. Dodds, P. S., and Danforth, C. M. 2009. Measuring the happiness of Large-Scale written expression: Songs, blogs, and presidents. Journal of Happiness Studies 116. Gilbert, E., and Karahalios, K. 2010. Widespread worry and the stock market. In Proceedings of the International Conference on Weblogs and Social Media . Greenspan, A. 2002. Remarks at the Bay Area coun- cil conference, San Francisco, California. http: // speeches/2002/20020111/default.%htm . Hamilton, J. D. 1994. Time Series Analysis . Princeton Uni- versity Press. Hopkins, D., and King, G. 2010. A method of automated nonparametric content analysis for social science. American Journal of Political Science 54(1):229–247. Koppel, M., and Shtrimberg, I. 2004. Good news or bad news? Let the market decide. In AAAI Spring Symposium on Exploring Attitude and Affect in Text: Theories and Ap- plications . Krosnick, J. A.; Judd, C. M.; and Wittenbrink, B. 2005. The measurement of attitudes. The Handbook of Attitudes 2176. Lavrenko, V.; Schmill, M.; Lawrie, D.; Ogilvie, P.; Jensen, D.; and Allan, J. 2000. Mining of concurrent text and time series. In Proceedings of the 6th ACM SIGKDD Int’l Confer- ence on Knowledge Discovery and Data Mining Workshop on Text Mining . Lindsay, R. 2008. Predicting polls with Lexicon. 55722687/predicting-polls-with-lex%icon . Ludvigson, S. C. 2004. Consumer confidence and consumer spending. The Journal of Economic Perspectives 18(2):29– 50. Manning, C. D.; Raghavan, P.; and Sch¨utze, H. 2008. In- troduction to Information Retrieval . Cambridge University Press, 1st edition.
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