Biden vs Trump: Modeling US General Elections Using BERT Language Model

Chandra, Rohitash; Saini, R.

doi:10.1109/access.2021.3111035

Cited by 27 publications

(12 citation statements)

References 43 publications

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“…Bag of words and TF-IDF was used to conduct the analysis and Bag of words outperformed TF-IDF. Other authors [19,20] also established that Twitter can be used as an election indicator, which has the ability to predict the favorite candidate of the people which intend to emerge as the winner before the election is conducted. The people gave positive opinions about Donald Trump in almost the whole states which gave a high signal of emerging the winner of the election which he won.…”

Section: Related Workmentioning

confidence: 99%

From Twitter to Aso-Rock: A Natural Language Processing Spotlight for Understanding Nigeria 2023 Presidential Election

Olabanjo¹,

Wusu²,

Asokere³

et al. 2022

Preprint

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Introduction: Social media platforms such as Facebook, LinkedIn, Twitter, among others have been used as a tool for staging protests, opinion polls, campaign strategy, medium of agitation and a place of interest expression especially during elections. Past studies have established people’s opinion elections using social media posts. The advent of state-of-the-art algorithms for unstructured text processing implies tremendous progress in natural language processing and understanding. Aim: In this work, a Natural Language framework is designed to understand Nigeria 2023 presidential election based on public opinion using Twitter dataset. Methods: Raw datasets concerning discourse around Nigeria 2023 elections from Twitter of 2,059,113 18 dimensions were collected. Sentiment analysis was performed on the preprocessed dataset using three different machine learning models namely: Long Short-Term Memory (LSTM) Recurrent Neural Network, Bidirectional Encoder Representations from Transformers (BERT) and Linear Support Vector Classifier (LSVC) models. Personal tweet analysis of the three candidates provided insight on their campaign strategies and personalities while public tweet analysis established the public’s opinion about them. The performance of the models was also compared using accuracy, recall, false positive rate, precision and F-measure. Results: LSTM model gave an accuracy, precision, recall, AUC and f-measure of 88%, 82.7%, 87.2% , 87.6% and 82.9% respectively; the BERT model gave an accuracy, precision, recall, AUC and f-measure of 94%, 88.5%, 92.5%, 94.7% and 91.7% respectively while the LSVC model gave an accuracy, precision, recall, AUC and f-measure of 73%, 81.4%, 76.4%, 81.2% and 79.2% respectively. Conclusion: The experimental results show that sentiment analysis and other Natural Language Processing tasks can aid in the understanding of the social media space. Results also revealed the leverage of each aspirant towards winning the election. We conclude that sentiment analysis can form a general basis for generating insights for election and modeling election outcomes.

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Section: Related Workmentioning

confidence: 99%

From Twitter to Aso-Rock: A Natural Language Processing Spotlight for Understanding Nigeria 2023 Presidential Election

Olabanjo¹,

Wusu²,

Asokere³

et al. 2022

Preprint

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“…Finally, a vision of future research on integrating advances in process definitions, modelling, and evaluation is also discussed, pointing out, among others, the need for better investigating the application of state-of-the-art machine learning approaches. Chandra et al [2] mentioned that social media plays a crucial role in shaping the worldview during election campaigns. Social media has been used as a medium for political campaigns and a tool for organizing protests; some of which have been peaceful, while others have led to riots.…”

Section: IImentioning

confidence: 99%

Opinion Mining For E-Commerce in Social Media Accounts

S¹,

V²,

Desvar³

et al. 2023

IJRASET

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The usage of the Internet has been growing rapidly, and in the field of natural language processing (NLP), sentiment analysis has become one of the most popular techniques. Using sentiment analysis, the emotional tone behind a body of text can be mined effectively for different occasions. Instagram has become a popular platform for people to buy and sell online. Nonetheless, some studies have found that frauds occurred as a result of buying and selling on the platform, such as when product quantity and specifications differed from what was claimed, a defective product was received, and other frauds involved Instagram sellers. Hence, trust is vital when customers are engaged in S-Commerce activities on Instagram, and there is a need for a trust model to evaluate the trustworthiness of sellers. Nowadays, people provide their feedback after a long time of usage only on social media platforms like Instagram. Therefore, these comments play a major role in helping people decide if the product will be beneficial for them over a long period of time. Mining such content to evaluate people’s sentiments can play a critical role in making decisions to keep the situation under control. {objective} Six different classifiers have been used to classify the data. The experiment achieved the highest accuracy of 76.7% with logistic regression. This study can be useful to determine the trustworthiness of the company.

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“…These are platforms where users can share their feelings which can be processed to generate vital information related to many areas such as healthcare, elections, reviews, and illnesses. Previous research suggests that understanding user behaviour, especially regarding the feelings expressed during elections, can indicate the outcome of elections (Chandra and Saini 2021 ).…”

Section: Dangerous Eventsmentioning

confidence: 99%

Detection of dangerous events on social media: a critical review

Jamil

Pais

Cordeiro

2022

Soc. Netw. Anal. Min.

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The usability of the events information on social media has been widely studied recently. Several surveys have reviewed the specific type of events on social media using various techniques. Most of the existing methods for event detection are segregated as they approach certain situations that limit the overall details of events happening consecutively on social media while ignoring the crucial relationship between the evolution of these events. Numerous events that materialize on the social media sphere every day before our eyes jeopardize people’s safety and are referred to by using a high-level concept of dangerous events. The front of dangerous events is broad, yet no known work exists that fully addresses and approaches this issue. This work introduces the term dangerous events and defines its scope in terms of practicality to establish the origins of the events caused by the previous events and their respective relationship. Furthermore, it divides dangerous events into sentiment, scenario, and action-based dangerous events grouped on their similarities. The existing research and methods related to event detection are surveyed, including some available events datasets and knowledge-base to address the problem. Finally, the survey is concluded with suggestions for future work and possible related challenges.

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Biden vs Trump: Modeling US General Elections Using BERT Language Model

Cited by 27 publications

References 43 publications

From Twitter to Aso-Rock: A Natural Language Processing Spotlight for Understanding Nigeria 2023 Presidential Election

From Twitter to Aso-Rock: A Natural Language Processing Spotlight for Understanding Nigeria 2023 Presidential Election

Opinion Mining For E-Commerce in Social Media Accounts

Detection of dangerous events on social media: a critical review

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