A proposed method for predicting US presidential election by analyzing sentiment in social media

Wicaksono, Andy Januar; Suyoto, '; Pranowo, Pranowo

doi:10.1109/icsitech.2016.7852647

Cited by 9 publications

(7 citation statements)

References 13 publications

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“…In this section, the proposed methods are tested on two Twitter datasets related to the 2016 and 2020 US presidential elections. They are compared to the methods introduced Yavari et al [5] Oueslati et al [12], Singh et al [13], Wang and Gan [14], Wicaksono [15] in terms of prediction accuracy and result. The proposed indicators and compared methods are all implemented with Python programming language in a computer system with Intel core i5 and 2 GB main memory specifications.…”

Section: Experiments and Resultsmentioning

confidence: 99%

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Election Prediction Based on Messages Feature Analysis in Twitter Social Network

Yavari

Hassanpour

2023

IJE

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With the emergence of virtual social networks, predicting social events such as elections using social network data has attracted the attention of researchers. In this paper, three indicators for election prediction have been proposed. First, the tweets are grouped based on a specific time window. Next, the indicator values for each candidate in each time window are calculated based on the sentiment scores and re-tweet numbers. In fact, the indicators are calculated based on the ratio of features related to positive to negative sentiments. Finally, using the aging estimation method, the indicator values for each party on the election date are predicted. The party with larger predicted indicator values will be considered as the winner. Investigations into Twitter data related to 2016 and 2020 US presidential elections on a four-month time span indicate that the indicator values and elections can be predicted with a high accuracy.

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Section: Experiments and Resultsmentioning

confidence: 99%

“…Wicaksono [15] proposed a method based on sentiment analysis using Equation ( 5), to predict the outcome of 2016 US presidential election. Based on this equation, the Success Rate (SR) of each party in an election is calculated, and the party that gets a higher score is predicted as the winner of the election:…”

Section: Literature Reviewmentioning

confidence: 99%

Election Prediction Based on Messages Feature Analysis in Twitter Social Network

Yavari

Hassanpour

2023

IJE

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“…The third method is introduced by Wicaksono [23]. In this method, the success rate of each party in the elections is calculated based on Equation (7).…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

Election Prediction Based on Sentiment Analysis using Twitter Data

2022

IJE

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Election prediction has always been of interest to many people. In the last decade, an increasing influence of social networks and the possibility of sharing opinions and ideas has rendered election prediction based on social network data analysis. This paper, drawing on Twitter data and sentiment analysis, uses the proportion of positive messages rate to negative messages rate as an effective indicator for predicting elections. Then, using the aging estimation method, it predicts the values of this indicator in future time windows. T he experiments conducted on Twitter data related to the 2020 United States presidential election in a four-month time window indicate that the indicator values and eventually the election results can be predicted with high accuracy.

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“…In a similar vein, Wicaksono et al [16] investigated the 2016 US Presidential Election using state level tweets related to political parties and candidates, proposing a sentiment analysis model combining the Binarized Multinomial Naïve Bayes Classifier, SentiWordNet, and AFINN-111. Their study shed light on the potential of sentiment analysis techniques to predict election outcomes at the state level, paving the way for further investigations.…”

Section: Related Studiesmentioning

confidence: 99%

From Social Media to Ballot Box: Leveraging Location-Aware Sentiment Analysis for Election Predictions

Khan,

Boudjellal,

Zhang

et al. 2023

CMC

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Predicting election outcomes is a crucial undertaking, and various methods are employed for this purpose, such as traditional opinion polling, and social media analysis. However, traditional polling approaches often struggle to capture the intricate nuances of voter sentiment at local levels, resulting in a limited depth of analysis and understanding. In light of this challenge, this study focuses on predicting elections at the state/regional level along with the country level, intending to offer a comprehensive analysis and deeper insights into the electoral process. To achieve this, the study introduces the Location-Based Election Prediction Model (LEPM), which utilizes social media data, specifically Twitter, and integrates location-aware sentiment analysis techniques at both the state/region and country levels. LEPM predicts the support and opposing strength of each political party/candidate. To determine the location of users/voters who have not disclosed their location information in tweets, the model utilizes a Voter Location Detection (VotLocaDetect) approach, which leverages recent tweets/posts. The sentiment analysis techniques employed in this study include rule-based sentiment analysis, Valence Aware Dictionary and Sentiment Reasoner (VADER) as well as transformers-based sentiment analysis such as Bidirectional Encoder Representations from Transformers (BERT), BERTweet, and Election based BERT (ElecBERT). This study uses the 2020 United States (US) Presidential Election as a case study. By applying the LEPM model to the election, the study demonstrates its ability to accurately predict outcomes in forty-one states, achieving an 0.84 accuracy rate at the state level. Moreover, at the country level, the LEPM model outperforms traditional polling results. With a low Mean Absolute Error (MAE) of 0.87, the model exhibits more precise predictions and serves as a successful alternative to conventional polls and other methodologies. Leveraging the extensive social media data, the LEPM model provides nuanced insights into voter behavior, enabling policymakers to make informed decisions and facilitating in-depth analyses of elections. The study emphasizes the importance of using social media data for reliable election prediction and offers implications for enhancing prediction accuracy and understanding voter sentiment and behavior.

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A proposed method for predicting US presidential election by analyzing sentiment in social media

Cited by 9 publications

References 13 publications

Election Prediction Based on Messages Feature Analysis in Twitter Social Network

Election Prediction Based on Messages Feature Analysis in Twitter Social Network

Election Prediction Based on Sentiment Analysis using Twitter Data

From Social Media to Ballot Box: Leveraging Location-Aware Sentiment Analysis for Election Predictions

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