Sentiment Analysis of before and after Elections: Twitter Data of U.S. Election 2020

Chaudhry, Hassan Nazeer; Javed, Yasir; Kulsoom, Farzana; Mehmood, Zahid; Khan, Zafar Iqbal; Shoaib, Umar; Janjua, Sadaf Hussain

doi:10.3390/electronics10172082

Cited by 40 publications

(26 citation statements)

References 47 publications

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“…The classification model based on sentiments is prepared to predict the inclination of tweets to infer the results of the elections. [13] analyzed Twitter sentiment of U.S. Election 2020 Twitter Data to determine public views before, during, and after elections and compared them with actual election results. They also compared opinions from the 2016 election in which Donald J. Trump was victorious with the 2020 election.…”

Section: Related Workmentioning

confidence: 99%

Location-based Sentiment Analysis of 2019 Nigeria Presidential Election Using A Voting Ensemble Approach

Onyenwe

Nwagbo

Orji

et al. 2022

Preprint

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Sentiment analysis is a natural language processing (NLP) tool that uses automatic methods to capture the opinions of the masses over social media such as Twitter, especially for election monitoring and predictions. However, recent researches have explored mining of public sentiment in tweets neglecting using tweets location features and ensemble learning of NLP. In this paper, we use 2019 Nigeria presidential election tweets to perform sentiment analysis through the application of a voting ensemble approach (VEA) in which the predictions from multiple techniques are combined to find the best polarity of a tweet (sentence). This is to determine public views on the 2019 Nigeria Presidential elections and compare them with actual election results. Our sentiment analysis experiment is focused on location-based viewpoints using Twitter location features. For this experiment, we live-streamed Nigeria 2019 election tweets via Twitter API to create tweets dataset of 583816 size, pre-processed the data, and applied VEA by utilizing three different Sentiment Classifiers to obtain the choicest polarity of a given tweet. Furthermore, we segmented our tweets dataset into Nigerian states and geopolitical zones, then plotted state-wise and geopolitical-wise user sentiments towards the major candidates (Buhari and Atiku) and their political parties. The overall objective of the use of states/geopolitical zones is to evaluate the similarity between the sentiment of location-based tweets compared to actual election results. The results reveal that whereas there are election outcomes that coincide with the sentiment expressed on Twitter social media in most cases as shown by the polarity scores of different locations, there are also some election results where our location analysis similarity test failed.

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Section: Related Workmentioning

confidence: 99%

Location-based Sentiment Analysis of 2019 Nigeria Presidential Election Using A Voting Ensemble Approach

Onyenwe

Nwagbo

Orji

et al. 2022

Preprint

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“…Studies have previously used Twitter to study communication and discussions during the U.S. Presidential elections [15][16][17][18][19]. Yaqub et al [20] performed sentiment analysis on Twitter data collected for ten days before and after the Election Day .…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2022

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We capture the public sentiment towards candidates in the 2020 US Presidential Elections, by analyzing 7.6 million tweets sent out between October 31st and November 9th, 2020. We apply a novel approach to first identify tweets and user accounts in our database that were later deleted or suspended from Twitter. This approach allows us to observe the sentiment held for each presidential candidate across various groups of users and tweets: accessible tweets and accounts, deleted tweets and accounts, and suspended or inaccessible tweets and accounts. We compare the sentiment scores calculated for these groups and provide key insights into the differences. Most notably, we show that deleted tweets, posted after the Election Day, were more favorable to Joe Biden, and the ones posted leading to the Election Day, were more positive about Donald Trump. Also, the older a Twitter account was, the more positive tweets it would post about Joe Biden. The aim of this study is to highlight the importance of conducting sentiment analysis on all posts captured in real time, including those that are now inaccessible, in determining the true sentiments of the opinions around the time of an event.

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“…For instance, one study used Twitter to report the effect of the COVID-19 pandemic on the sleep quality of pregnant women based on 192 tweets [18]. The sentiment analysis of social media content is a challenging task, since such texts are unstructured, brief, informal, and casual; are prone to mistakes in dictation and grammar; and are noisy (emojis, hashtags, URLs, etc); and they entail ambiguities, such as polysemy [19]. Therefore, using artificial intelligence and machine learning tools and techniques may prove to be beneficial for tackling these challenges.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, using artificial intelligence and machine learning tools and techniques may prove to be beneficial for tackling these challenges. Among these tools are advanced, analytical natural language processing (NLP) algorithms called transformers [19][20][21][22][23][24][25][26]. They are newly proposed tools and extensions to previous versions of a deep artificial neural network-recurrent neural networks-for language modeling and language encoding.…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis of Insomnia-Related Tweets via a Combination of Transformers Using Dempster-Shafer Theory: Pre– and Peri–COVID-19 Pandemic Retrospective Study

Maghsoudi¹,

Nowakowski²,

Agrawal³

et al. 2022

J Med Internet Res

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Background The COVID-19 pandemic has imposed additional stress on population health that may result in a change of sleeping behavior. Objective In this study, we hypothesized that using natural language processing to explore social media would help with assessing the mental health conditions of people experiencing insomnia after the outbreak of COVID-19. Methods We designed a retrospective study that used public social media content from Twitter. We categorized insomnia-related tweets based on time, using the following two intervals: the prepandemic (January 1, 2019, to January 1, 2020) and peripandemic (January 1, 2020, to January 1, 2021) intervals. We performed a sentiment analysis by using pretrained transformers in conjunction with Dempster-Shafer theory (DST) to classify the polarity of emotions as positive, negative, and neutral. We validated the proposed pipeline on 300 annotated tweets. Additionally, we performed a temporal analysis to examine the effect of time on Twitter users’ insomnia experiences, using logistic regression. Results We extracted 305,321 tweets containing the word insomnia (prepandemic tweets: n=139,561; peripandemic tweets: n=165,760). The best combination of pretrained transformers (combined via DST) yielded 84% accuracy. By using this pipeline, we found that the odds of posting negative tweets (odds ratio [OR] 1.39, 95% CI 1.37-1.41; P<.001) were higher in the peripandemic interval compared to those in the prepandemic interval. The likelihood of posting negative tweets after midnight was 21% higher than that before midnight (OR 1.21, 95% CI 1.19-1.23; P<.001). In the prepandemic interval, while the odds of posting negative tweets were 2% higher after midnight compared to those before midnight (OR 1.02, 95% CI 1.00-1.07; P=.008), they were 43% higher (OR 1.43, 95% CI 1.40-1.46; P<.001) in the peripandemic interval. Conclusions The proposed novel sentiment analysis pipeline, which combines pretrained transformers via DST, is capable of classifying the emotions and sentiments of insomnia-related tweets. Twitter users shared more negative tweets about insomnia in the peripandemic interval than in the prepandemic interval. Future studies using a natural language processing framework could assess tweets about other types of psychological distress, habit changes, weight gain resulting from inactivity, and the effect of viral infection on sleep.

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Sentiment Analysis of before and after Elections: Twitter Data of U.S. Election 2020

Cited by 40 publications

References 47 publications

Location-based Sentiment Analysis of 2019 Nigeria Presidential Election Using A Voting Ensemble Approach

Location-based Sentiment Analysis of 2019 Nigeria Presidential Election Using A Voting Ensemble Approach

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

Sentiment Analysis of Insomnia-Related Tweets via a Combination of Transformers Using Dempster-Shafer Theory: Pre– and Peri–COVID-19 Pandemic Retrospective Study

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