Sentiment Analysis on YouTube Comments in MPL Season 13 Tournament Using Ensemble Machine Learning Method
Analisis Sentimen pada Komentar YouTube dalam Turnamen MPL Season 13 Dengan Metode Ensemble Machine learning
DOI:
https://doi.org/10.21070/ups.6938Keywords:
Sentiment, Ensemble Machine Learning, Preprocessing, Classification, MPL Season 13Abstract
This study conducted sentiment analysis on YouTube comments related to MPL Season 13 using an Ensemble Learning method. The aim was to identify sentiment patterns and team popularity based on positive support. Data was collected using the YouTube Data API v3, resulting in 6,424 comments, which after preprocessing were reduced to 5,185 comments, consisting of 3,131 positive and 2,064 negative comments. The SMOTE oversampling technique was used to address class imbalance. Classification was performed using a majority voting method from several algorithms. The results showed an accuracy of 86.70 percent for hard voting and 86.17 percent for soft voting. The labeling process combined automatic and manual methods, improving classification accuracy. The analysis revealed EVOS had the highest number of supporters with 877, followed by RRQ with 743 and ONIC with 556. This study contributes to the development of sentiment analysis in e-sports and opens opportunities for further research.
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