Comparative Study Of Public Sentiment Analysis On Ikn (Nusantara Capital City) Across X And Youtube Using Pso-Optimized Svm
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Abstract
This study aims to compare public sentiment regarding the IKN on Platform X and YouTube using a Polynomial Kernel SVM algorithm optimized by PSO. The method used is an experimental study. Data was obtained through web scraping using Google Colab, yielding 1,413 tweets for the X dataset and 814 for the YouTube dataset. The collected data underwent data cleaning, followed by sentiment labeling of each data point into three classes. Following this, TF-IDF vectorization, PSO-based feature selection, and classification using the Polynomial Kernel SVM were performed. The results of the study showed an accuracy of 76% for the PSO-SVM on the X platform and 75% for the YouTube platform. These results indicate that the PSO-SVM algorithm performs better on the X platform compared to the YouTube platform.
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