Inferring of Political Leaning from Tweets, Retweets of Indian politics
G. Krishna Kishore1, Suresh Babu Dasari2, S. Ravi Kishan3

1Dr.G. Krishna Kishore, Computer Science and Engineering, VRSEC, Vijayawada, India.
2Suresh Babu Dasari, Computer Science and Engineering, VRSEC, Vijayawada, India.
3S. Ravi Kishan, Computer Science and Engineering, VRSEC, Vijayawada, India.

Manuscript received on September 16, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 5462-5466 | Volume-8 Issue-12, October 2019. | Retrieval Number: K22590981119/2019©BEIESP | DOI: 10.35940/ijitee.K2259.1081219
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Abstract: The current use of on-line social networks to unfold information and change opinions, by using most of the people, news media and political actors alike, has implement new outlet of research in computational political science. Here The trouble of compute and inferring the political leaning of Twitter customers. We formulate political leaning inference as a convex optimization problem that consists of ideas: Twitter users generally tend to tweet and retweet constantly, and Similar Twitter users have a tendency to be retweeted through similar sets of target audience. Then take a look at our inference technique to a massive dataset of Indian political personnel’s related individual tweets amassed over a time frame. On a fixed of regularly retweeted resources, our method achieves a few percentage of accuracy and excessive rank correlation compared with manually created labels. By analyzing the political leaning of some amount regularly retweeted property, and get regular clients who retweeted them, and the hash tags utilized by those sources, our quantitative have a examine sheds slight at the political demographics of the Twitter population, and the temporal dynamics of political polarization as activities spread..
Keywords: Convex Programming, Signal Processing, Support Vector Machine (SVM), Twitter.
Scope of the Article: Advanced Computing Architectures and New Programming Models