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

Abstract

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.

Keywords


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

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References

L. M. Heidbreder, I. Bablok, S. Drews, and C. Menzel, “Tackling the plastic problem: A review on perceptions, behaviors, and interventions,” Sci. Total Environ., vol. 668, pp. 1077–1093, Jun. 2019, doi: 10.1016/j.scitotenv.2019.02.437.

M. Liu, X. Luo, and W.-Z. Lu, “Public perceptions of environmental, social, and governance (ESG) based on social media data: Evidence from China,” J. Clean. Prod., vol. 387, p. 135840, Feb. 2023, doi: 10.1016/j.jclepro.2022.135840.

P. Jiang, J. Zhou, Y. Van Fan, J. J. Klemeš, M. Zheng, and P. S. Varbanov, “Data analysis of resident engagement and sentiments in social media enables better household waste segregation and recycling,” J. Clean. Prod., vol. 319, p. 128809, Oct. 2021, doi: 10.1016/j.jclepro.2021.128809.

P. D. I. Yogyakarta, “DIY Terus Upayakan Perbaikan Pengelolaan Sampah.” https://jogjaprov.go.id/berita/diy-terus-upayakan-perbaikan-pengelolaan-sampah (accessed Oct. 17, 2023).

D. D. I. Yogyakarta, “Ketua Komisi A Ingatkan Pentingnya Selesaikan Masalah Sampah Perkotaan dari Hulu.” https://www.dprd-diy.go.id/ketua-komisi-a-ingatkan-pentingnya-selesaikan-masalah-sampah-perkotaan-dari-hulu/ (accessed Oct. 17, 2023).

B. K. dan P. S. D. Manusia, “Mengatasi Darurat Sampah Di Kota Yogyakarta Kreativitas Menuju Kebersihan Yang Memikat Hati.” https://bkpsdm.jogjakota.go.id/detail/index/28545 (accessed Oct. 17, 2023).

S. Rahman, N. Jahan, F. Sadia, and I. Mahmud, “Social crisis detection using Twitter based text mining-a machine learning approach,” Bull. Electr. Eng. Informatics, vol. 12, no. 2, pp. 1069–1077, Apr. 2023, doi: 10.11591/eei.v12i2.3957.

N. Khalid, S. Abdul-Rahman, W. Wibowo, N. A. S. Abdullah, and S. Mutalib, “Leveraging social media data using latent dirichlet allocation and naïve bayes for mental health sentiment analytics on Covid-19 pandemic,” Int. J. Adv. Intell. Informatics, vol. 9, no. 3, 2023, doi: https://doi.org/10.26555/ijain.v9i3.1367.

A. Samih, A. Ghadi, and A. Fennan, “Enhanced sentiment analysis based on improved word embeddings and XGboost,” Int. J. Electr. Comput. Eng., vol. 13, no. 2, p. 1827, Apr. 2023, doi: 10.11591/ijece.v13i2.pp1827-1836.

N. Mohamad Sham and A. Mohamed, “Climate Change Sentiment Analysis Using Lexicon, Machine Learning and Hybrid Approaches,” Sustainability, vol. 14, no. 8, p. 4723, Apr. 2022, doi: 10.3390/su14084723.

N. R. Bhowmik, M. Arifuzzaman, M. R. H. Mondal, and M. S. Islam, “Bangla Text Sentiment Analysis Using Supervised Machine Learning with Extended Lexicon Dictionary,” Nat. Lang. Process. Res., vol. 1, no. 3–4, p. 34, 2021, doi: 10.2991/nlpr.d.210316.001.

M. Mujahid et al., “Sentiment Analysis and Topic Modeling on Tweets about Online Education during COVID-19,” Appl. Sci., vol. 11, no. 18, p. 8438, Sep. 2021, doi: 10.3390/app11188438.

F. Rustam, M. Khalid, W. Aslam, V. Rupapara, A. Mehmood, and G. S. Choi, “A performance comparison of supervised machine learning models for Covid-19 tweets sentiment analysis,” PLoS One, vol. 16, no. 2, p. e0245909, Feb. 2021, doi: 10.1371/journal.pone.0245909.

A. A. Reshi et al., “COVID-19 Vaccination-Related Sentiments Analysis: A Case Study Using Worldwide Twitter Dataset,” Healthcare, vol. 10, no. 3, p. 411, Feb. 2022, doi: 10.3390/healthcare10030411.

H. Zhao, Z. Liu, X. Yao, and Q. Yang, “A machine learning-based sentiment analysis of online product reviews with a novel term weighting and feature selection approach,” Inf. Process. Manag., vol. 58, no. 5, p. 102656, Sep. 2021, doi: 10.1016/j.ipm.2021.102656.

R. S. Jagdale, V. S. Shirsat, and S. N. Deshmukh, “Sentiment Analysis on Product Reviews Using Machine Learning Techniques,” 2019, pp. 639–647. doi: 10.1007/978-981-13-0617-4_61.

S. A. S. Neshan and R. Akbari, “A Combination of Machine Learning and Lexicon Based Techniques for Sentiment Analysis,” in 2020 6th International Conference on Web Research (ICWR), Apr. 2020, pp. 8–14. doi: 10.1109/ICWR49608.2020.9122298.

A. M. Rahat, A. Kahir, and A. K. M. Masum, “Comparison of Naive Bayes and SVM Algorithm based on Sentiment Analysis Using Review Dataset,” in 2019 8th International Conference System Modeling and Advancement in Research Trends (SMART), Nov. 2019, pp. 266–270. doi: 10.1109/SMART46866.2019.9117512.

A. T. Mahmood, S. S. Kamaruddin, R. K. Naser, and M. M. Nadzir, “A Combination of Lexicon and Machine Learning Approaches for Sentiment Analysis on Facebook,” J. Syst. Manag. Sci., vol. 10, no. 3, pp. 140–150, Sep. 2020, doi: 10.33168/JSMS.2020.0310.

Z. Nasim, Q. Rajput, and S. Haider, “Sentiment analysis of student feedback using machine learning and lexicon based approaches,” in 2017 International Conference on Research and Innovation in Information Systems (ICRIIS), Jul. 2017, pp. 1–6. doi: 10.1109/ICRIIS.2017.8002475.

Y. Pratama, D. T. Murdiansyah, and K. M. Lhaksmana, “Analisis Sentimen Kendaraan Listrik Pada Media Sosial Twitter Menggunakan Algoritma Logistic Regression dan Principal Component Analysis,” J. Media Inform. Budidarma, vol. 7, no. 1, pp. 529–535, 2023, doi: http://dx.doi.org/10.30865/mib.v7i1.5575.

Q. He, “Recent Works for Sentiment Analysis using Machine Learning and Lexicon Based Approaches,” in 2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE), Apr. 2022, pp. 422–426. doi: 10.1109/AEMCSE55572.2022.00090.

V. A. Rao, K. Anuranjana, and R. Mamidi, “A Sentiwordnet Strategy for Curriculum Learning in Sentiment Analysis,” 2020, pp. 170–178. doi: 10.1007/978-3-030-51310-8_16.

F. ˚Arup Nielsen, “Afinn Project.” https://www2.imm.dtu.dk/pubdb/edoc/imm6975.pdf (accessed Oct. 17, 2023).

F. Å. Nielsen, “AFINN Sentiment Lexicon.” AFINN Sentiment Lexicon (accessed Oct. 17, 2023).

F. Å. Nielsen, “A new ANEW: Evaluation of a word list for sentiment analysis in microblogs,” in Proceedings of the ESWC2011 Workshop on “Making Sense of Microposts”: Big things come in small packages 718 in CEUR Workshop Proceedings, 2011, pp. 93–98. doi: https://doi.org/10.48550/arXiv.1103.2903.

C. Hutto and E. Gilbert, “VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text,” in Proceedings of the International AAAI Conference on Web and Social Media, May 2014, vol. 8, no. 1, pp. 216–225. doi: 10.1609/icwsm.v8i1.14550.

A. Amin, I. Hossain, A. Akther, and K. M. Alam, “Bengali vader: A sentiment analysis approach using modified vader,” in 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), 2019, pp. 1–6.

S. Loria and others, “textblob Documentation,” Release 0.15, vol. 2, no. 8, p. 269, 2018.

T. De Smedt and W. Daelemans, “Pattern for python,” J. Mach. Learn. Res., vol. 13, no. 1, pp. 2063–2067, 2012.

S. M. Mohammad and P. D. Turney, “Nrc emotion lexicon,” Natl. Res. Counc. Canada, vol. 2, p. 234, 2013.

Z. Keita, “Social Media Sentiment Analysis In Python With VADER — No Training Required!,” 2022. https://towardsdatascience.com/social-media-sentiment-analysis-in-python-with-vader-no-training-required-4bc6a21e87b8 (accessed Aug. 01, 2023).

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