Naive Bayes Performance in Analysis of Public Opinion Sentiment Against COVID-19

Authors

  • Ayu Hendrati Rahayu Politeknik TEDC Bandung
  • Ari Sudrajat Politeknik TEDC Bandung

DOI:

https://doi.org/10.33633/jais.v7i3.7134

Abstract

The huge impact caused by the COVID-19 pandemic has made many people express their opinions on Twitter social media. There are various responses given by the community that are negative and positive. The dataset comes from kaggle with more than 750 tweets of data. Classification designed by the Naive Bayes method. Implementation through preprocessing, case folding, tokenizing, stopword removal, TF-IDF, and cross validation has been able to produce quite high accuracy. After classification, validation will be carried out with Cross Fold Validation. The best value is on cv5 where accuracy = 0.847, precision = 0.855, recall = 0.83, and f1 score = 0.842.

References

J. Samuel, G. G. M. N. Ali, M. M. Rahman, E. Esawi, dan Y. Samuel, “COVID-19 public sentiment insights and machine learning for tweets classification,” Inf., vol. 11, no. 6, hal. 1–22, 2020.

R. Vijay, B. Vangara, K. Thirupathur, dan S. P. Vangara, “Opinion Mining Classification using Naive Bayes Algorithm,” Int. J. Innov. Technol. Explor. Eng., vol. 9, no. 5, hal. 495–498, 2020.

V. M., J. Vala, dan P. Balani, “A Survey on Sentiment Analysis Algorithms for Opinion Mining,” Int. J. Comput. Appl., vol. 133, no. 9, hal. 7–11, 2016.

N. S. Sattar dan S. Arifuzzaman, “Covid-19 vaccination awareness and aftermath: Public sentiment analysis on twitter data and vaccinated population prediction in the usa,” Appl. Sci., vol. 11, no. 13, 2021.

A. M. Almars, E. S. Atlam, T. H. Noor, G. ELmarhomy, R. Alagamy, dan I. Gad, “Users opinion and emotion understanding in social media regarding COVID-19 vaccine,” Computing, vol. 104, no. 6, hal. 1481–1496, 2022.

A. Umair dan E. Masciari, “Sentimental and spatial analysis of COVID-19 vaccines tweets,” J. Intell. Inf. Syst., 2022.

S. A. Jafar Zaidi, I. Chatterjee, dan S. Brahim Belhaouari, “COVID-19 Tweets Classification during Lockdown Period Using Machine Learning Classifiers,” Appl. Comput. Intell. Soft Comput., vol. 2022, hal. 1–8, Jul 2022.

F. M. J. M. Shamrat et al., “Sentiment analysis on twitter tweets about COVID-19 vaccines using NLP and supervised KNN classification algorithm,” Indones. J. Electr. Eng. Comput. Sci., vol. 23, no. 1, hal. 463–470, 2021.

N. G. Ramadhan dan F. D. Adhinata, “Sentiment analysis on vaccine COVID-19 using word count and Gaussian Naïve Bayes,” Indones. J. Electr. Eng. Comput. Sci., vol. 26, no. 3, hal. 1765, 2022.

M. Myslín, S.-H. Zhu, W. Chapman, dan M. Conway, “Using Twitter to Examine Smoking Behavior and Perceptions of Emerging Tobacco Products,” J. Med. Internet Res., vol. 15, no. 8, hal. e174, Agu 2013.

K. B. Priya Iyer dan S. Kumaresh, “Twitter sentiment analysis on coronavirus outbreak using machine learning algorithms,” Eur. J. Mol. Clin. Med., vol. 7, no. 3, hal. 2663–2676, 2020.

N. M. Abdulkareem, A. Mohsin Abdulazeez, D. Qader Zeebaree, dan D. A. Hasan, “COVID-19 World Vaccination Progress Using Machine Learning Classification Algorithms,” Qubahan Acad. J., vol. 1, no. 2, hal. 100–105, 2021.

Z. Ali, S. K. Shahzad, dan W. Shahzad, “Performance Analysis of Statistical Pattern Recognition Methods in KEEL,” Procedia Comput. Sci., vol. 112, no. 2017, hal. 2022–2030, 2017.

P. Sharma dan T.-S. Moh, “Prediction of Indian election using sentiment analysis on Hindi Twitter,” in 2016 IEEE International Conference on Big Data (Big Data), 2016, hal. 1966–1971.

K. Chandel, V. Kunwar, S. Sabitha, T. Choudhury, dan S. Mukherjee, “A comparative study on thyroid disease detection using K-nearest neighbor and Naive Bayes classification techniques,” CSI Trans. ICT, vol. 4, no. 2–4, hal. 313–319, 2016.

T. Mustaqim, K. Umam, dan M. A. Muslim, “Twitter text mining for sentiment analysis on government’s response to forest fires with vader lexicon polarity detection and k-nearest neighbor algorithm,” J. Phys. Conf. Ser., vol. 1567, no. 3, hal. 8–15, 2020.

W. B. Zulfikar, M. Irfan, C. N. Alam, dan M. Indra, “The comparation of text mining with Naive Bayes classifier, nearest neighbor, and decision tree to detect Indonesian swear words on Twitter,” in 2017 5th International Conference on Cyber and IT Service Management, CITSM 2017, 2017.

P. Liu, H. han Zhao, J. yu Teng, Y. yan Yang, Y. feng Liu, dan Z. wei Zhu, “Parallel naive Bayes algorithm for large-scale Chinese text classification based on spark,” J. Cent. South Univ., vol. 26, no. 1, hal. 1–12, 2019.

K. M. A. Hasan, M. S. Sabuj, dan Z. Afrin, “Opinion mining using Naïve Bayes,” in 2015 IEEE International WIE Conference on Electrical and Computer Engineering, WIECON-ECE 2015, 2016, hal. 511–514.

V. Dhanalakshmi dan D. Bino, “Opinion mining from student feedback data using supervised learning algorithms,” in 2016 3rd MEC International Conference on Big Data and Smart City, ICBDSC 2016, 2016, hal. 1–5.

S. Dey Sarkar, S. Goswami, A. Agarwal, dan J. Aktar, “A Novel Feature Selection Technique for Text Classification Using Naïve Bayes,” Int. Sch. Res. Not., vol. 2014, hal. 1–10, 2014.

S. Kaur, G. Sikka, dan L. K. Awasthi, “Sentiment Analysis Approach Based on N-gram and KNN Classifier,” ICSCCC 2018 - 1st Int. Conf. Secur. Cyber Comput. Commun., hal. 13–16, 2018.

B. Bhutani, N. Rastogi, P. Sehgal, dan A. Purwar, “Fake News Detection Using Sentiment Analysis,” 2019 12th Int. Conf. Contemp. Comput. IC3 2019, hal. 1–5, 2019.

K. Poddar, G. B. D. Amali, dan K. S. Umadevi, “Comparison of Various Machine Learning Models for Accurate Detection of Fake News,” 2019 Innov. Power Adv. Comput. Technol. i-PACT 2019, hal. 1–5, 2019.

F. C. Permana, Y. Rosmansyah, dan A. S. Abdullah, “Naive Bayes as opinion classifier to evaluate students satisfaction based on student sentiment in Twitter Social Media,” J. Phys. Conf. Ser., vol. 893, no. 1, 2017.

Downloads

Published

2022-12-28