Sentiment Analysis of Political Discussion on Twitter in Nigeria 2019 Presidential Election

2022 ◽  
Vol 14 (2) ◽  
pp. 1
Author(s):  
Aderonke A. Oni ◽  
Samuel Oni ◽  
Blessing Udemezue
Keyword(s):  
Sentiment Analysis ◽  
Presidential Election ◽  
Political Discussion

scholarly journals A Novel Method for Twitter Sentiment Analysis Based on Attentional-Graph Neural Network

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 92 ◽  
Author(s):  
Mingda Wang ◽  
Guangmin Hu
Keyword(s):  
Neural Network ◽  
Sentiment Analysis ◽  
Presidential Election ◽  
Loss Functions ◽  
Neural Structure ◽  
Text Information ◽  
Novel Method ◽  
Structural Controllability ◽  
Graph Neural Networks ◽  
Multiple Metrics

Twitter sentiment analysis is an effective tool for various Twitter-based analysis tasks. However, there is still no neural-network-based research which takes both the tweet-text information and user-connection information into account. To this end, we propose the Attentional-graph Neural Network based Twitter Sentiment Analyzer (AGN-TSA), a Twitter sentiment analyzer based on attentional-graph neural networks. AGN-TSA fuses the tweet-text information and the user-connection information through a three-layered neural structure, which includes a word-embedding layer, a user-embedding layer and an attentional graph network layer. For the training of AGN-TSA, dedicated loss functions are designed for the structural controllability of AGN-TSA network. Experiments based on real-world dataset concerning the 2016 presidential election of America exhibit that AGN-TSA is superior under multiple metrics over several prevailing methods, with a performance boost of over 5%. The empirical settings of parameters are given based on extensive rotation experiments.

scholarly journals Opinion and Sentiment Analysis of Twitter Users during the 2021 Ecuador Presidential Election

2021 ◽  
Author(s):  
Jorge Parraga-Alava ◽  
Jorge Rodas-Silva ◽  
Iván Quimi ◽  
Roberth Alcivar-Cevallos
Keyword(s):  
Sentiment Analysis ◽  
Presidential Election ◽  
Twitter Users

scholarly journals An Analysis of Fake Narratives on Social Media during 2019 Indonesian Presidential Election

2020 ◽  
Vol 36 (4) ◽  
pp. 351-368
Author(s):  
Vience Mutiara Rumata ◽  
◽  
Fajar Kuala Nugraha ◽  
Keyword(s):  
Social Media ◽  
Presidential Election ◽  
Political Polarization ◽  
Political Discussion ◽  
Fake News ◽  
Data Set ◽  
Political Situation ◽  
Radical Group ◽  
Time Frames ◽  
Election Studies

Social media become a public sphere for political discussion in the world, with no exception in Indonesia. Social media have broadened public engagement but at the same time, it creates an inevitable effect of polarization particularly during the heightened political situation such as a presidential election. Studies found that there is a correlation between fake news and political polarization. In this paper, we identify and the pattern of fake narratives in Indonesia in three different time frames: (1) the Presidential campaign (23 September 2018 -13 April 2019); (2) the vote (14-17 April 2019); (3) the announcement (21-22 May 2019). We extracted and analyzed a data-set consisting of 806,742 Twitter messages, 143 Facebook posts, and 16,082 Instagram posts. We classified 43 fake narratives where Twitter was the most used platform to distribute fake narratives massively. The accusation of Muslim radical group behind Prabowo and Communist accusation towards the incumbent President Joko Widodo were the two top fake narratives during the campaign on Twitter and Facebook. The distribution of fake narratives to Prabowo was larger than that to Joko Widodo on those three platforms in this period. On the contrary, the distribution of fake narratives to Joko Widodo was significantly larger than that to Prabowo during the election and the announcement periods. The death threat of Joko Widodo was top fake narratives on these three platforms. Keywords: Fake narratives, Indonesian presidential election, social media, political polarization, post.

scholarly journals Sharing and Commenting Facilitate Political Learning on Facebook: Evidence From a Two-Wave Panel Study

2021 ◽  
Vol 7 (3) ◽  
pp. 205630512110478
Author(s):  
Dam Hee Kim ◽  
Brian E. Weeks ◽  
Daniel S. Lane ◽  
Lauren B. Hahn ◽  
Nojin Kwak
Keyword(s):  
Social Media ◽  
Presidential Election ◽  
Media Use ◽  
Panel Study ◽  
Political Knowledge ◽  
Political Information ◽  
Political Discussion ◽  
Mediation Model ◽  
Political Learning ◽  
Political News

Social media, as sources of political news and sites of political discussion, may be novel environments for political learning. Many early reports, however, failed to find that social media use promotes gains in political knowledge. Prior research has not yet fully explored the possibility based on the communication mediation model that exposure to political information on social media facilitates political expression, which may subsequently encourage political learning. We find support for this mediation model in the context of Facebook by analyzing a two-wave survey prior to the 2016 U.S. presidential election. In particular, sharing and commenting, not liking or opinion posting, may facilitate political knowledge gains.

“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.

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