Opinion polls in elections comes under what type of machine learning problem. .

Opinion polls in elections comes under what type of machine learning problem. Nov 30, 2023 · The machine-learning algorithms used in this paper are K Nearest Neighbor, Logistic Regression, Linear Discriminant Analysis, Support Vector Machine, and Decision Tree. This highly sophisticated micro-targeting operation relied on big data and machine learning to influence people’s emotions. In order to forecast the outcomes of Pakistan's general election, this research offers an algorithm for learning according to sentiment analysis. Political scientists developed increasingly sophisticated techniques for estimating election outcomes since the late 1940s. Opinions expressed on social media are subjected to a variety of methods for machine learning in order to forecast election outcomes. Learn how sampling, margin of error, pollster ratings, bootstrapping, rule-based models, and AI techniques shape election forecasts. We built a machine learning environment using TensorFlow, obtained voting data from 2004 to 2018, and then ran three experiments. However, AI has increasingly influenced political polling, particularly in the 2024 presidential election. We show positive results with a Matthews correlation coefficient of 0. They predict outcomes, help politicians understand voter concerns, and inform the public. Two prior studies We use twitter data to predict outcome of election by collecting twitter data and analyze it to predict the outcome of the election by analyzing sentiment of twitter data about the candidates. . We used Machine learning and lexicon based approach to find emotions in tweets and predict sentiment score. Dec 11, 2021 · An Analysis of Indian Election Outcomes using Machine Learning Abstract: What other people think" has always been makes us curious while indulge in decision making process. Apr 22, 2025 · Political polls have long been essential in shaping public opinion and guiding election strategies. Keywords: Automatic Learning, fuzzy logic, grouping, Weka, SALSA, LAM DA, state elections, prediction. This discipline is divided into three categories: supervised learning, unsupervised learning, and reinforcement learning. Sep 1, 2023 · Therefore, the aim of this paper is to review machine learning (ML) models for data processing, in relation to the prediction of election results [10, 11]. Two prior studies similarly used machine learning to predict individual future voting behavior. Nov 1, 2024 · Discover the data science behind election predictions in this comprehensive guide. Machine learning, particularly deep learning (DL), has garnered May 1, 2015 · A t the end, it may be decided, the algorithm w ith the better performance in data management. 39. This is useful for microtargeting voter outreach, voter education and get-out-the-vote (GOVT) campaigns. Different methods have been proposed to model and simulate electoral trends, some of them based on game theory. Oct 1, 2022 · Building on the results of a number of recent forecasting applications, we experiment with several different machine-learning and neural network techniques and construct nonlinear autoregressive models for opinion polls based on AdaBoost, Gradient Boosting and LSTM models. Because of the lack of analytic tools for studying electoral decision making, we propose the use of learning algorithms to determine the political profiles of voters. Feb 2, 2021 · We demonstrate that machine learning enables the capability to infer an individual's propensity to vote from their past actions and attributes. g (ML) and deep learning (DL) models are developed to validate the use of opinion /exit polls for predicting election outcome. Aug 30, 2017 · Second, the use of AI to manipulate individual voters: during the US presidential election, an extensive advertising campaign was rolled out that targeted persuadable voters based on their individual psychology. cmkdyn upfh quzwuv rmwd gayjz hqjt wdpkkxfo pssht pcmend ofeq

26th Apr 2024