A large-scale sentiment analysis of tweets pertaining to the 2020 US presidential election

Ali, Rao Hamza; Pinto, Gabriela; Lawrie, Evelyn; Linstead, Erik

doi:10.1186/s40537-022-00633-z

Cited by 11 publications

(4 citation statements)

References 29 publications

(26 reference statements)

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“…LMNN is a distance metric learning algorithm that has demonstrated better performance in location-content awareness (Chen et al, 2020a). LSTM is an improved recurrent neural network architecture; it has demonstrated applicability in geographical visualization based on text content classification (Hashemi, 2023).…”

Section: Evaluation and Geoparsing Resultsmentioning

confidence: 99%

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Risk identification of public opinion on social media: a new approach based on cross-spatial network analysis

Li,

Xu,

Riaz

et al. 2024

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Purpose This study aims to use geographical information on social media for public opinion risk identification during a crisis. Design/methodology/approach This study constructs a double-layer network that associates the online public opinion with geographical information. In the double-layer network, Gaussian process regression is used to train the prediction model for geographical locations. Second, cross-space information flow is described using local government data availability and regional internet development indicators. Finally, the structural characteristics and information flow of the double-layer network are explored to capture public opinion risks in a fine-grained manner. This study used the early stages of the COVID-19 outbreak for validation analyses, and it collected more than 90,000 pieces of public opinion data from microblogs. Findings In the early stages of the COVID-19 outbreak, the double-layer network exhibited a radiating state, and the information dissemination was more dependent on the nodes with higher in-degree. Moreover, the double-layer network structure showed geographical differences. The risk contagion was more significant in areas where information flow was prominent, but the influence of nodes was reduced. Originality/value Public opinion risk identification that incorporates geographical scenarios contributes to enhanced situational awareness. This study not only effectively extends geographical information on social media, but also provides valuable insights for accurately responding to public opinion.

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Section: Evaluation and Geoparsing Resultsmentioning

confidence: 99%

“…, 2020a). LSTM is an improved recurrent neural network architecture; it has demonstrated applicability in geographical visualization based on text content classification (Hashemi, 2023).…”

Section: Results and Analysismentioning

confidence: 99%

Risk identification of public opinion on social media: a new approach based on cross-spatial network analysis

Li,

Xu,

Riaz

et al. 2024

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“…Ebrahimi et al [39] state that dealing with sarcasm in a sentiment analysis task is an open research issue that requires more work, especially on how to deal with sarcastic tweets in the context of training and prediction phases. Even with all the challenges, sentiment analysis is an important part of providing useful insights into how political conversations during major events are conducted on a social platform like X [40].…”

Section: Sentiment Analysis Of Electionsmentioning

confidence: 99%

Polarizing Topics on Twitter in the 2022 United States Elections

Katalinić,

Dunđer,

Seljan

2023

Information

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Politically polarizing issues are a growing concern around the world, creating divisions along ideological lines, which was also confirmed during the 2022 United States midterm elections. The purpose of this study was to explore the relationship between the results of the 2022 U.S. midterm elections and the topics that were covered during the campaign. A dataset consisting of 52,688 tweets in total was created by collecting tweets of senators, representatives and governors who participated in the elections one month before the start of the elections. Using unsupervised machine learning, topic modeling is built on the collected data and visualized to represent topics. Furthermore, supervised machine learning is used to classify tweets to the corresponding political party, whereas sentiment analysis is carried out in order to detect polarity and subjectivity. Tweets from participating politicians, U.S. states and involved parties were found to correlate with polarizing topics. This study hereby explored the relationship between the topics that were creating a divide between Democrats and Republicans during their campaign and the 2022 U.S. midterm election outcomes. This research found that polarizing topics permeated the Twitter (today known as X) campaign, and that all elections were classified as highly subjective. In the Senate and House elections, this classification analysis showed significant misclassification rates of 21.37% and 24.15%, respectively, indicating that Republican tweets often aligned with traditional Democratic narratives.

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“…Classification methods for sentiment analysis towards political candidates, such as presidents, were suggested by (Ali, 2022), who recommended techniques like Naive Bayes, Support Vector Machine (SVM), or deep learning models such as Convolutional Neural Network (CNN) or Long Short Term Memory (LSTM) for large-scale sentiment analysis on tweets. However, the extensive volume of Twitter data presents challenges in processing and analysis and difficulties in handling sarcasm and slang.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Classification Methods in Sentiment Analysis: The Impact of Feature Selection and Ensemble Techniques Optimization

Defit,

Windarto,

Alkhairi

2024

telematika.

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Optimizing classification methods (forward selection, backward elimination, and optimized selection) and ensemble techniques (AdaBoost and Bagging) are essential for accurate sentiment analysis, particularly in political contexts on social media. This research compares advanced classification models with standard ones (Decision Tree, Random Tree, Naive Bayes, Random Forest, K- NN, Neural Network, and Generalized Linear Model), analyzing 1,200 tweets from December 10-11, 2023, focusing on "Indonesia" and "capres." It encompasses 490 positive, 355 negative, and 353 neutral sentiments, reflecting diverse opinions on presidential candidates and political issues. The enhanced model achieves 96.37% accuracy, with the backward selection model reaching 100% accuracy for negative sentiments. The study suggests further exploration of hybrid feature selection and improved classifiers for high-stakes sentiment analysis. With forward feature selection and ensemble method, Naive Bayes stands out for classifying negative sentiments while maintaining high overall accuracy (96.37%).

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A large-scale sentiment analysis of tweets pertaining to the 2020 US presidential election

Cited by 11 publications

References 29 publications

Risk identification of public opinion on social media: a new approach based on cross-spatial network analysis

Risk identification of public opinion on social media: a new approach based on cross-spatial network analysis

Polarizing Topics on Twitter in the 2022 United States Elections

Comparative Analysis of Classification Methods in Sentiment Analysis: The Impact of Feature Selection and Ensemble Techniques Optimization

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