A Multifaceted Method for Election Prediction on Multiple Social Media Platforms Using Machine Learning
Keywords:
Election Prediction, Social media platforms, machine learning, ensembled learning, Natural language processingAbstract
The paper presents a Multifaceted Method for Election Prediction on Multiple Social Media Platforms Using Machine Learning" . It is a novel approach to forecasting election outcomes by leveraging data from various social media platforms like Facebook, twitter and YouTube. . By employing machine learning algorithms, the method integrates diverse social media signals, such as user sentiment, engagement metrics, and topic trends, to create a comprehensive prediction model. The model has optimize sentiment analysis as well as use of proposed ensemble approach. This multifaceted approach aims to improve the accuracy of election predictions by capturing a wider array of public opinions and behaviors across different social media ecosystems. The study demonstrates the potential of this method through case studies and empirical analysis, highlighting its effectiveness. Evaluation of proposed method with existing models have been conducted and found that proposed method got good accuracy over others. It is approximate 94.27 ℅ for combined data and proposed ensemble approach also have good accuracy over state of art methods.
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