election prediction algorithm
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A Hybrid Method of Sentiment Analysis and Machine Learning
Election forecasting approaches particularly in terms of state-level predictions It provides a valuable foundation for future research in this field and contributes to advancing our under-standing of election dynamics Index Terms—Election Prediction Sentiment Analysis X (Twitter) Machine Learning Location-based Data I INTRODUCTION |
What is presidential election forecasting?
Abstract—U.S. Presidential Election forecasting has been a re-search interest for several decades. Currently, election prediction consists of two main approaches: traditional models that incor-porate economic data and poll surveys, and models that leverage X and other social media platforms due to their increasing popularity in the past decade.
How accurate are election predictions?
In the testing data (2007 and beyond), the results improve slightly, predicting 81.9% of the election outcomes correctly, again with a better record at predicting when the incumbent party is likely to win (fig. S5).
Can machine learning predict Pakistan's general election results?
Numerous machine learning approaches are applied to opinions shared on social media for predicting election results. This paper presents a machine learning model based on sentiment analysis to predict Pakistan's general election results. In a general election, voters vote for their favorite party or candidate based on their personal interests.
What is the proposed election results prediction framework?
The proposed election results prediction framework predicts Twitter opinions of the general election 2018 in Pakistan. The tweets were posted about current trends of the political party, which users in hashtags consider to express their views. The received tweets are stored in the database, and the dataset is pre-processed.
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Prediction and analysis of Indonesia Presidential election from
The authors make an algorithm and method to count important data top words and train the model and predict the polarity of the sentiment. The experimental |
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Forecasting elections results via the voter model with stubborn nodes
We are able to perform time-evolving estimates of the model parameters and use these to forecast the vote shares for each party in any election. We obtain a |
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2020 Presidential Election Model
2 sept. 2019 The models predict whether the incumbent presidential candidate will win the popular vote in each state and the District of Columbia and thus ... |
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An Improved Model for Predicting Presidential Election Outcomes
the final Gallup Poll before the election. oped a forecasting model which combines ... predicting the outcomes of presidential elections. |
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A Bayesian Model for the Prediction of United States Presidential
the model had the same Electoral College Prediction as FiveThirtyEight. The method of using state and national polls as a prior in election prediction seems |
| Classification Using C4. 5 Algorithm in Election Participation |
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Modeling and forecasting US presidential election using learning
obtain an accurate forecasting model for the US presiden- tial election. To identify a reliable model artificial neural networks (ANN) and support vector |
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Attribute analysis with classification algorithm on election participation
29 mai 2020 Naive Bayes algorithm for prediction classes in the Presidential Election DPD |
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An Algorithm for Estimating Turnout as a Guide to Predicting Elections
substantive change is a reformulation of the summary algorithm used to predict turnout. 2 An election prediction based on the raw marginals. |
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Prediction for the 2020 United States Presidential Election using
3 nov. 2020 The proposed Lasso Regression model forecasts that the vote share for the incumbent Republican Party to be 41.63% in 2020 US presidential ... |
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Modeling and forecasting US presidential election using - EconStor
The primary objective of this paper is to model and forecast the United States presidential election via the usage of learning algorithms Political and economic |
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An Algorithm for Estimating Turnout as a Guide to Predicting Elections
Crespi's (1988) study of 423 preelection polls found them to be quite inaccurate, missing the actual division of the vote by almost 6 percent- age points on |
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Analyzing Political Opinions and Prediction of Voting - CORE
In this paper, we aim to identify the political preferences and tendency of the US populations using classification and data mining techniques To provide the |
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Utilizing Learning Algorithms to Improve News - CORE
Improving Electoral Predictions with Learning Algorithms 16 candidates for election forecasts, and specifically the role of polling data regarding issues |
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2020 Presidential Election Model - Moodys Analytics
2 sept 2019 · The models predict whether the incumbent presidential candidate will win the popular vote in each state and the District of Columbia, and thus the |
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Apriori-based Prediction of the Multi Seat Elections - IEEE Xplore
than the poll model The improved poll model by delegating and weighting operations can predict election results of Hongkong Legislative Council more |
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The machine learning in the prediction of elections
the algorithm with the better performance in data management Keywords: Automatic Learning, fuzzy logic, grouping, Weka, SALSA, LAMDA, state elections |
![The Economist US presidential forecast\](https://media.springernature.com/lw685/springer-static/image/art%3A10.1186%2Fs40537-020-00334-5/MediaObjects/40537_2020_334_Fig1_HTML.png "The Economist US presidential forecast\")
