Twitter is a miniaturized scale blogging service in which individuals share and talk about their contemplations and perspectives in 140 characters without being obliged by space and time. A huge number of tweets are produced every day on diverse issues. Social researchers network have distinguished a few connections and measurements that actuate homophily. Assessments or feelings towards various issues have been seen as a key measurement which describes human conduct. Individuals typically express their assumptions towards different issues. Diverse people from various strolls of social life may impart same insight towards different issues. At the point when these people constitute a gathering, such gatherings can be advantageously named same wavelength groups or gatherings. That is, same wavelength groups will be bunches framed on the premise of conclusions and suppositions of comparable tint towards different issues by various people. Such same wave length groups crucially associate the people in an important and intentional organization.

Twitter, blogging, data, Politics

[1]. Balahur, A., Steinberger, R., Goot, E. V. D., Pouliquen, B., &Kabadjov, M. “Opinion mining on newspaper quotations” Web Intelligence and Intelligent Agent Technologies, 2009. WI-IAT`09. IEEE/WIC/ACM International Joint Conferences on (Vol. 3, pp. 523-526). IET.
[2]. David, MB, Andrew, YN & Michael IJ 2003, Latent Dirichlet Allocation , Journal of Machine Learning Research, vol. 3, pp.993-1022.
[3]. Sriram, B, David, F & Murat D 2010, Short Text Classification in Twitter to Improve Information Filtering ACM SIGIR, Geneva, Switzerland, pp. 841-842.
[4]. Bermingham and A. F. Smeaton, “ On using Twitter to monitor political sentiment and predict election results,” in n: Sentiment Analysis where AI meets Psychology (SAAIP) Workshop at the International Joint Conference for Natural Language Processing (IJCNLP), Chiang Mai, Thailand, 2011.
[5]. Potts, Christopher 2011, On the negativity of negation. In Proceedings of Semantics and Linguistic Theory 20, Ithaca, NY, CLC Publications, pp. 636-659.
[6]. Cambria, E & Hussain, A 2013, Sentic Computing: Techniques, Tools and applications Springer briefs in cognitive computation.
[7]. Pang, B & Lee, L 2008, ‘opinion mining and sentiment analysis’ Foundations and Trends in Information Retrieval, vol. 2, no. 1, pp. 1 135.
[8]. Abbasi, MA, Chai, S, liu, H & Sagoo, K 2012, Real -world behavior analysis through a social media : 5th International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction (SBP 2012), USA, pp. 18-26.
[9]. Michal Skuza,Andrzej Romanowski, "Sentiment Analysis of Twitter Data within Big Data Distributed Environment for Stock Prediction", Computer Science and Information Systems pp. 1349– 1354, 2015 F230 ACSIS, Vol.5.
[10].Venkata Sasank Pagolu, Kamal Nayan Reddy Challa, Ganapati Panda,Babita Majhi, "Sentiment Analysis of Twitter Data for Predicting Stock Market Movements", International conference on Signal Processing, Communication, Power and Embedded System (SCOPES), 2016.
[11].Tina Ding, Vanessa Fang, Daniel Zuo, "Stock Market Prediction based on Time Series Data and Market Sentiment", 2012.
[12]. Phillip Tichaona Sumbureru, "Analysis of Tweets for Prediction of Indian Stock Markets", International Journal of Science and Research (IJSR), Volume 4 Issue 8, August 2015.
[13]. Rishabh Soni, K. James Mathai, "Improved Twitter Sentiment Prediction through „Cluster-then-Predict Model‟", International Journal of Computer Science and Network, Volume 4, Issue 4, August 2015
[14]. Linhao Zhang, "Sentiment Analysis on Twitter with Stock Price and Significant Keyword Correlation", April 16, 2013.