Sentiment Analysis of the Waste Problem based on YouTube comments using VADER and Deep Translator

 (*)Herman Yuliansyah Mail (Universitas Ahmad Dahlan, Yogyakarta, Indonesia)
 Surahma Asti Mulasari (Universitas Ahmad Dahlan, Yogyakarta, Indonesia)
 Sulistyawati Sulistyawati (Universitas Ahmad Dahlan, Yogyakarta, Indonesia)
 Fanani Arief Ghozali (Universitas Ahmad Dahlan, Yogyakarta, Indonesia)
 Bambang Sudarsono (Universitas Ahmad Dahlan, Yogyakarta, Indonesia)

(*) Corresponding Author

Submitted: October 19, 2023; Published: February 2, 2024


The waste problem is a severe problem that significantly affects the environment and public health. To effectively determine the public’s perception of the waste problem, it is necessary to examine public sentiment toward waste management. This research aims to develop a sentiment analysis model using VADER and deep-translator and analyze the Yogyakarta waste emergency problem. This research was conducted in two phases, namely, the first phase was developing a sentiment analysis model by evaluating its performance based on public data. Then, the second phase classifies public comments from YouTube regarding the waste problem to understand public perceptions and evaluations by identifying positive, negative, and neutral sentiments. The model evaluation results show that sentiment analysis using VADER and deep translator can achieve Accuracy, Precision, Recall, and F1-score values of 0.716, 0.837, 0.853, and 0.738, respectively. The sentiment results from YouTube comments obtained positive, neutral, and negative sentiments of 30.0%, 31.7%, and 37.3%, respectively. The results of the sentiment analysis are neutral sentiment discussing waste management, disappointment in negative sentiment, and hope for waste management in positive sentiment.


Waste Problem, Sentiment Analysis; Lexicon-Based; VADER; Deep-Translator

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