Estimation of the 2020 US Presidential Election Competition and Election Stratagies The 2020 US presidential election is still more than a year away, but the media is noisy due to the continuous registration of candidates that will face Trump in the election. Trump has already started to check is rivals through media. So far, Joe Biden and Bernie Sanders seem to have to most possibility to face Trump in the election. Sensitivity analysis was conducted to the data collected from Twitter from the year 2019. The positivity scores have been proved to effect approval ratings, they are estimated to effect the likeliness of becoming the most popular candidate. The data was compared to the past election from 2008, 2012, and 2016. The elections included the past rival background of Obama and McCain, Obama and Romney, Trump and Clinton to show how positive ratings effect the election. Tweets were collected through HTML and Python. The collected data was analyzed using SPSS and MS Excel. Data was defined into three major statuses; positive, negative, and neutral by a lexicon named Valence Aware Dictionary and Sediment Reasoner (VADER). The null hypothesis was rejected through Independent Sample TTest, MannWhitney U Test, Kruskal Wallis Test to show the difference between means. Research results show who will become Trumpu0027s estimated competitor for the 2020 election.

Can twitter analytics predict election outcome? An insight from 2017 Punjab assembly elections Abstract   Since the beginning of this decade, there has seen an exponential growth in number of internet users using social media, especially Twitter for sharing their views on various topics of common interest like sports, products, politics etc. Due to the active participation of large number of people on Twitter, huge amount of data (i.e. big data) is being generated, which can be put to use (after refining) to analyze real world problems. This paper takes into consideration the Twitter data related to the 2017 Punjab (a state of India) assembly elections and applies different social media analytic techniques on collected tweets to extract and unearth hidden but useful information. In addition to this, we have employed machine learning algorithm to perform polarity analysis and have proposed a new seat forecasting method to accurately predict the number of seats that a political party is likely to win in the elections. Our results confirmed that Indian National Congress was likely to emerge winner and that in fact was the outcome, when results got declared.