Page 127 - Invited Paper Session (IPS) - Volume 2

IPS 188 G. P. Samanta
https://research.google.com/googleblogs/pdfs/
google_predicting_the_present.pdf).
10. Choi, Hyunyoung and Hal Varian (2009b), “Predicting Initial Claims for
Unemployment Insurance Using Google Trends”, Technical Report,
Google Inc. (Website:
https://research.google.com/archive/papers/initialclaimsUS.pdf).
11. Choi, Hyunyoung and Hal Varian (2012), “Predicting the Present with
Google Trends”, Economic Record, The Economic Society of
Australia, Vol. 88, Issue S1 (Special Issue), pp 2-9.
12. Ettredge, Michael, John Gerdes and Gilbert Karuga (2005), “Using
Web-based Search Data to Predict Macroeconomic Statistics”,
Communications of the ACM, Vol 48, No. 11, November, pp. 87-92.
13. Ellen Tobback, Stefano Nardelli, David Martens (2017), “Between
Hawks and Doves: Measuring Central Bank Communication”, Working
Paper Series, European Central Bank, No. 2086, July.
14. Guzmán, Giselle (2011), “Internet Search Behavior as an Economic
Forecasting Tool: The Case of Inflation Expectations”, Journal of
Economic and Social Measurement, Vol. 36, No. 3, IOS Press, pp.
119-167.
15. Kharde, Vishal A. and S.S.Sonawane (2016), “Sentiment Analysis of
Twitter Data: A Survey of Techniques”, International Journal of
Computer Applications, Vol. 139, No. 11, April, pp. 5-15.
16. Mika V. Mäntylä, Daniel Graziotin, Miikka Kuutila (2018), “The
Evolution of Sentiment Analysis—A Review of Research Topics,
Venues, and Top Cited Papers”, Computer Science Review, Volume
27, February 2018, pp. 16-32.
17. Seabold, Skipper and Andrea Coppola (2015), “Nowcasting Prices
Using Google Trends – An Application to Central America”, Policy
Research Working Paper 7398, World Bank Group, August.

114 | I S I W S C 2 0 1 9