Sentiment Analysis on Predicting Presidential Election: Twitter Used Case

2020 ◽  
pp. 105-117
Author(s):  
Nedaa Baker Al Barghuthi ◽  
Huwida E. Said
Keyword(s):  
Sentiment Analysis ◽  
Presidential Election

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

The 2016 U.S. Presidential Election and Transition Events: A Social Media Volume and Sentiment Analysis

2019 ◽  
Vol 84 (3) ◽  
pp. 196-209 ◽  
Author(s):  
Brandon Boatwright ◽  
Joseph P Mazer ◽  
Sarah Beach
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Presidential Election ◽  
Media Volume

“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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