CHRISTANTO, ERIC (2025) PREDICTING POLITICAL ELECTION USING CONVOLUTIONAL NEURAL NETWORK:ANALYSING PUBLIC SENTIMENT ON SOCIAL MEDIA PLATFORM. S1 thesis, UNIVERSITAS KATOLIK SOEGIJAPRANATA.

Preview	Text 20.K1.0013-ERIC CHRISTANTO-COVER_a.pdf Download (763kB) | Preview
	Text 20.K1.0013-ERIC CHRISTANTO-ISI_a.pdf Restricted to Registered users only Download (1MB)
Preview	Text 20.K1.0013-ERIC CHRISTANTO-DAPUS_a.pdf Download (881kB) | Preview
	Text 20.K1.0013-ERIC CHRISTANTO-LAMP_a.pdf Restricted to Registered users only Download (846kB)

Abstract

In the modern era, social media has become a powerful source of information that reflects public opinion and sentiments. Take twitter for example nowadays known as X, with its vast user base and active engagement during major events such as a campaign, offers a rich and valuable dataset to understand public opinion for the ongoing event. Convolutional Neural Networks (CNN), a type of deep learning algorithm, have the capability to capture local context in natural language processing, making them well-suited for analyzing the nature of human language in social media conversations. This study aims to explore the potential of CNNs in predicting election outcomes based on Twitter data, now X . Keyword: Convolutional Neural Networks, CNNs, Sentiment Analysis, Twitter, X.

Actions (login required)

View Item