Twitter Sentiment Analysis Based on US Presidential Election 2016

Ramasubbareddy, Somula; Kumar, K. Dinesh; Aravindharamanan, S.; Govinda, K.

doi:10.1007/978-981-13-9282-5_34

Cited by 27 publications

(10 citation statements)

References 4 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

View full text Add to dashboard Cite

show abstract

“…Bag of words and TF-IDF were used to conduct the analysis and Bag of words outperformed TF-IDF. Other authors [17,18] also established that Twitter can be used as an election indicator and has the ability to predict the favorite candidate of the people to emerge as the winner before the election is conducted. The people gave positive opinions about Donald Trump in almost all states in the United States prior to election.…”

Section: Related Workmentioning

confidence: 99%

Twitter Sentiment Analysis of Lagos State 2023 Gubernatorial Election using BERT

Wusu¹,

Olabanjo²,

Padonu³

et al. 2023

Preprint

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Election outcomes have been predicted in the past with the help of various state-of-the-art language models. Sentiment analysis helps in establishing the opinions of the public about a particular subject, a popular experiment known as opinion mining. Twitter has grown in popularity and proven to be a key tool in mining people’s sentiments concerning election and other trending subjects of interest. The outcome of the just concluded Presidential election in Nigeria shifts the focus on Lagos State governorship election. In this study, we propose a Bidirectional Encoder Representations from Transformers (BERT) model for the sentiment analysis of governorship election in Lagos State Nigeria using Twitter data. A total of 800,000 personal and public tweets were scraped from twitter concerning the three prominent contesting candidates using carefully selected search queries. The tweets were preprocessed to avoid noise and inconsistencies. The preprocessed tweets were passed into the pretrained and finetuned BERT model. The result was analyzed to establish the sentiments of the public about the candidates. The social networks of the candidates were also analyzed. The parameter-tuning yield different results with different learning rates (LR). Results showed that the learning rate at 1e-7 gave the best performance and that the smaller the learning rate, the higher the accuracy but the larger the epoch size, the higher the accuracy.

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“…This was done to help customers find the most branded smartphones in India. Somula et al (2020) proposed an approach to perform a sentiment analysis to determine the winner of the US election in 2016, the tweets were collected if they mentioned Donald Trump and Hillary Clinton, the analysis of the tweets revealed that Donald Trump received more positive scores as compared to Hillary Clinton. Mehta et al (2020) proposed a sentiment analysis approach to identify the best Indian airlines through Twitter, the analysis of results revealed that the customers were happier with Air India services compared to SpiceJet.…”

Section: Related Workmentioning

confidence: 99%

Public Perception of the Fifth Generation of Cellular Networks (5G) on Social Media

et al. 2021

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With the advancement of social media networks, there are lots of unlabeled reviews available online, therefore it is necessarily to develop automatic tools to classify these types of reviews. To utilize these reviews for user perception, there is a need for automated tools that can process online user data. In this paper, a sentiment analysis framework has been proposed to identify people’s perception towards mobile networks. The proposed framework consists of three basic steps: preprocessing, feature selection, and applying different machine learning algorithms. The performance of the framework has taken into account different feature combinations. The simulation results show that the best performance is by integrating unigram, bigram, and trigram features.

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Twitter Sentiment Analysis Based on US Presidential Election 2016

Cited by 27 publications

References 4 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

Twitter Sentiment Analysis of Lagos State 2023 Gubernatorial Election using BERT

Public Perception of the Fifth Generation of Cellular Networks (5G) on Social Media

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