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Forecasting Election Data Using Regression Models and Sentimental Analysis
S. Gazali,
Published in Springer Science and Business Media Deutschland GmbH
Volume: 688
Pages: 501 - 509
Predictive Analytics is emerging as one of the popular branch of Machine Learning with huge amount of money spent on to research, build and test models to improve the accuracy of the outcome. In our paper we have implemented different regression models such as Logistic Regression, Support Vector Machines, Naive Bayes Classifier, and Neural Networks to forecast the result of the US Presidential election 2016. Social media websites have become a very popular communication platform among users. Millions of users share their opinions, extend their support, and vent their anger on various government policies and on different aspects of their life. Hence, social media websites contain huge amount of data for sentimental analysis. We have narrowed our attention toward Twitter, the most popular microblogging website for performing sentimental analysis using the opinions shared by the users on twitter. © 2021, Springer Nature Singapore Pte Ltd.
About the journal
JournalData powered by TypesetLecture Notes in Electrical Engineering
PublisherData powered by TypesetSpringer Science and Business Media Deutschland GmbH