Zaira Cindya Dwynne, -
COMPARISON OF MACHINE LEARNING ALGORITHMS ON
SENTIMENT ANALYSIS OF ELSAGATE CONTENT.
INTERNATIONAL CONFERENCE ON SMART COMPUTING, IOT AND MACHINE LEARNING.
(Submitted)
Abstract
This study responds to the increasing
phenomenon of elsagate content on various platforms, especially
on YouTube Kids and YouTube, which are often accessed by
children. Elsagate content contains sensitive elements for
children such as horror, sexuality, and violence. The
community's response to this content varies, from positive to
negative, to neutral, especially on platforms like YouTube. The
main purpose of this research is to understand the opinions of
the YouTube community regarding children's content with
unclear meanings or containing elsagate elements. Using 2452
data, this study applies five machine learning algorithms to
classify sentiment: Naive Bayes Classifier (NBC), Random
Forest (RF), Support Vector Machine (SVM), K-Nearest
Neighbors (K-NN), and Logistic Regression (LR). The research
results show that data division with a 90:10 ratio provides the
best performance. The Support Vector Machine algorithm
achieves the highest accuracy of 63%, with precision of 61%,
recall of 63%, and an F1-score of 60%. On the other hand, the
K-Nearest Neighbors algorithm shows the lowest performance with an accuracy of 56%, precision of 55%, recall of 56%, and an F1-score of 55%. Thus, besides aiming to provide insights into elsagate, this research also highlights the performance of Support Vector Machine in analyzing sentiment towards elsagate content
Actions (login required)
 |
View Item |
