US presidential election 2020 prediction based on Twitter data using lexicon-based sentiment analysis

2021 ◽  
Author(s):  
Deni Kurnianto Nugroho
Keyword(s):  
Sentiment Analysis ◽  
Presidential Election ◽  
Twitter Data ◽  
Us Presidential Election

“Too many Americans are trapped in fear, violence and poverty”: a psychology-informed sentiment analysis of campaign speeches from the 2016 US Presidential Election

2018 ◽  
Vol 4 (1) ◽  
Author(s):  
Thomas Hoffmann
Keyword(s):  
New York ◽  
Sentiment Analysis ◽  
Presidential Election ◽  
Negative Polarity ◽  
Psychological Research ◽  
Crowd Sourcing ◽  
Fine Grained ◽  
Campaign Speeches ◽  
Us Presidential Election

AbstractMost automatic sentiment analyses of texts tend to only employ a simple positive-negative polarity to classify emotions. In this paper, I illustrate a more fine-grained automatic sentiment analysis [Jockers, Matthew. 2016. Introduction to the Syuzhet package. https://cran.r-project.org/web/packages/syuzhet/vignettes/syuzhet-vignette.html (accessed 07 March 2017).; Mohammad, Saif M. & Peter D. Turney. 2013. Crowd sourcing a word-emotion association lexicon. Computational Intelligence 29(3). 436–465.] that is based on a classification of human emotions that has been put forward by psychological research [Plutchik, Robert. 1994. The psychology and biology of emotion. New York, NY: HarperCollins College Publishers.]. The advantages of this approach are illustrated by a sample study that analyses the emotional sentiment of the campaign speeches of the two main candidates of the 2016 US presidential election.

Is Donald Trump’s Presidential Election Victory a Surprise According to Twitter Data?

2017 ◽  
Vol 5 (1) ◽  
pp. 484
Author(s):  
Prof. Dr. Ayşe OĞUZLAR ◽  
Yusuf Murat KIZILKAYA
Keyword(s):  
Emotional Expression ◽  
Presidential Election ◽  
Positive Emotion ◽  
Negative Emotion ◽  
Emotion Expression ◽  
Emotion Words ◽  
Positive Feeling ◽  
Twitter Data ◽  
The Us ◽  
Us Presidential Election

NodeXL Pro is a software developed for network analysis and visualization. NodeXL Pro connects to twitter and extracts tweets about the topics that are set, and makes various analyzes with these tweets. In this study, during the US presidential election held on November 8, 2016, the tweets about the candidates were handled and sentiment analysis was performed on these twitters. When you look at tags, on November 8, 2016, the most popular tags on twitter were; hillaryclinton and trump. Instantaneous; hillaryclinton’s twits number were 24407, compared to trump labeled twets number were 4132. When tweets under both labels were examined, it was seen that the majority of the twitters did not have words with emotional expression. On the other hand, hillaryclinton labeled tweets; 1761 positive emotion words were found and 828 negative emotion words were detected. It is known that Trump had focused on social media throughout the campaign period. Although the instant twet number of the trump tag was less than the hillaryclinton tag, the number of words expressing positive emotion was 5411 and the number of words expressing negative emotion was 1659 in these twets. For Hillary Clinton, the ratio of the number positive emotion words to the number of negative emotion expression words was 2,12, about Trump while the rate of the number of positive emotion words to negative emotion words was 3.26 in tweets. In hillaryclinton-tagged tweets, with the most popular positive words; Proud, love, worked, win and wins, most popular negative words; Hate, collapse, corruption, lies and f..k. In trump-tagged, for the most popular positive; "wins, win, defeat, good, trust, amazing, supporter and work" words, for the most popular negatively; "badly, refuses, lost, f..k, hell, loses and dump" words were the most common words. When word pairs are examined; The hillaryclinton word was used in combination with the most potsword (612 times) and the word with beyonce (603 times). Again, in the twets with hillaryclinton tag positively emotional sentences the "proud" and "same" words had been used together (139 times), "worked" and "toward" words (130 times) . In twitler expressing negative emotion; The words "collapse" and hillaryclintons have been used together (29 times), "corruption" and "looks" (28 times), "lies" and "vote" words (19 times). Trump tagged twets; The trump word was mostly used; with the Donald word (563 times), vote word (198 times) and wins word (169 times). When you look at the tweets that were triggered by the Trump tag and express a positive feeling; Most of the words "trump" and "wins" (169 times), "trump" and "supporters" had been used together (123 times). When you review negative tweets that are trump labeled; The words "refuses" and "allow" (57 times), "hell" and "out" (43 times) were used together. Despite the fact that when trump and hillaryclinton-tagged twits were emotionally analyzed, the number of tweets about Trump was much less than the number of tweets about Clinton. It seems that, the number of positive emotion expression words in tweets about trump were too much in terms of the number of positive emotion words in tweets about Clinton. It is seen that the words that express positive and negative emotions about Trump and Clinton are generally very different from each other.

scholarly journals COVIDSenti: A Large-Scale Benchmark Twitter Data Set for COVID-19 Sentiment Analysis

2021 ◽  
pp. 1-13
Author(s):  
Usman Naseem ◽  
Imran Razzak ◽  
Matloob Khushi ◽  
Peter W. Eklund ◽  
Jinman Kim
Keyword(s):  
Sentiment Analysis ◽  
Large Scale ◽  
Data Set ◽  
Twitter Data

Identity propaganda: Racial narratives and disinformation

2021 ◽  
pp. 146144482110292
Author(s):  
Madhavi Reddi ◽  
Rachel Kuo ◽  
Daniel Kreiss
Keyword(s):  
Presidential Election ◽  
Power Relations ◽  
Vice President ◽  
Power Structures ◽  
Marginalized Groups ◽  
Identity Based ◽  
Us Presidential Election ◽  
Social Orders ◽  
Racial Narratives

This article develops the concept of “identity propaganda,” or narratives that strategically target and exploit identity-based differences in accord with pre-existing power structures to maintain hegemonic social orders. In proposing and developing the concept of identity propaganda, we especially aim to help researchers find new insights into their data on misinformation, disinformation, and propaganda by outlining a framework for unpacking layers of historical power relations embedded in the content they analyze. We focus on three forms of identity propaganda: othering narratives that alienate and marginalize non-white or non-dominant groups; essentializing narratives that create generalizing tropes of marginalized groups; and authenticating narratives that call upon people to prove or undermine their claims to be part of certain groups. We demonstrate the utility of this framework through our analysis of identity propaganda around Vice President Kamala Harris during the 2020 US presidential election.

scholarly journals Strengthening the EU’s resilience to hybrid threats

European View ◽  
2021 ◽  
pp. 178168582110046
Author(s):  
Sandra Kalniete ◽  
Tomass Pildegovičs
Keyword(s):  
Case Studies ◽  
Presidential Election ◽  
Turning Point ◽  
Hybrid Methods ◽  
Cyber Attacks ◽  
Political Will ◽  
Member States ◽  
Eu Member States ◽  
Us Presidential Election ◽  
Hybrid Threats

Against the backdrop of the deterioration of EU–Russia relations in recent years, there has been a shift in the awareness of hybrid threats all across the Union. At the same time, there is evidence of a growing political will to strengthen resilience to these threats. While hostile foreign actors have long deployed hybrid methods to target Europe, Russia’s intervention in Ukraine in 2014, interference in the 2016 US presidential election, and repeated cyber-attacks and disinformation campaigns aimed at EU member states have marked a turning point, exposing Western countries’ unpreparedness and vulnerability to these threats. This article analyses the EU’s resilience to hybrid warfare from institutional, regulatory and societal perspectives, with a particular focus on the information space. By drawing on case studies from member states historically at the forefront of resisting and countering Russian-backed disinformation campaigns, this article outlines the case for a whole-of-society approach to countering hybrid threats and underscores the need for EU leadership in a standard-setting capacity.

Obama's Reelection Prospects under “Bread and Peace” Voting in the 2012 US Presidential Election

2012 ◽  
Vol 45 (04) ◽  
pp. 635-639 ◽  
Author(s):  
Douglas A. Hibbs
Keyword(s):  
Presidential Election ◽  
Presidential Elections ◽  
Weighted Average ◽  
Armed Forces ◽  
Average Growth ◽  
Us Military ◽  
Us Presidential Election ◽  
Per Capita ◽  
Objectively Measured ◽  
Foreign Wars

According to the Bread and Peace Model postwar, American presidential elections should be interpreted as a sequence of referendums on the incumbent party's record during its four-year mandate period. In fact postwar aggregate votes for president are well explained by just two objectively measured fundamental determinants: (1) weighted-average growth of per capita real disposable personal income over the term, and (2) cumulative US military fatalities due to unprovoked, hostile deployments of American armed forces in foreign wars. No other outside variable systematically affects postwar aggregate votes for president.

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