Sentiment Analyst on Twitter Using the K-Nearest Neighbors (KNN) Algorithm Against Covid-19 Vaccination
Abstract
Full Text:
PDFReferences
D. Ukkaz, “SENTIMENT ANALYSIS OF COVID-19 VACCINE WITH DEEP LEARNING,” J. Theor. Appl. Inf. Technol., vol. 100, no. 12, pp. 4513–4521, 2022.
N. M. Abdulkareem, A. Mohsin Abdulazeez, D. Qader Zeebaree, and D. A. Hasan, “COVID-19 World Vaccination Progress Using Machine Learning Classification Algorithms,” Qubahan Acad. J., vol. 1, no. 2, pp. 100–105, 2021.
A. Winanto and C. Budihartanti, “Comparison of the Accuracy of Sentiment Analysis on the Twitter of the DKI Jakarta Provincial Government during the COVID-19 Vaccine Time,” J. Comput. Sci. an Eng., vol. 3, no. 1, pp. 14–27, 2022.
N. A. Azeez, O. E. Victor, and U. E. Junior, “SENTIMENT ANALYSIS OF COVID-19 TWEETS,” FUDMA J. Sci., vol. 5, no. 1996, p. 6, 2021.
R. K. BANIA, “Heterogeneous Ensemble Learning Framework for Sentiment Analysis on COVID-19 Tweets,” INFOCOMP, vol. 20, no. 02, 2021.
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, pp. 463–470, 2021.
Pristiyono, M. Ritonga, M. A. Al Ihsan, A. Anjar, and F. H. Rambe, “Sentiment analysis of COVID-19 vaccine in Indonesia using Naïve Bayes Algorithm,” IOP Conf. Ser. Mater. Sci. Eng., vol. 1088, no. 1, p. 012045, 2021.
N. G. Ramadhan and 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, p. 1765, 2022.
D. A. Nurdeni, I. Budi, and A. B. Santoso, “Sentiment Analysis on Covid19 Vaccines in Indonesia: From the Perspective of Sinovac and Pfizer,” 3rd 2021 East Indones. Conf. Comput. Inf. Technol. EIConCIT 2021, pp. 122–127, 2021.
N. S. Sattar and 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.
S. Nyawa, D. Tchuente, and S. Fosso-Wamba, “COVID-19 vaccine hesitancy: a social media analysis using deep learning,” Ann. Oper. Res., 2022.
A. M. Almars, E. S. Atlam, T. H. Noor, G. ELmarhomy, R. Alagamy, and I. Gad, “Users opinion and emotion understanding in social media regarding COVID-19 vaccine,” Computing, vol. 104, no. 6, pp. 1481–1496, 2022.
A. Umair and E. Masciari, “Sentimental and spatial analysis of COVID-19 vaccines tweets,” J. Intell. Inf. Syst., 2022.
S. Hota and S. Pathak, “KNN classifier based approach for multi-class sentiment analysis of twitter data,” Int. J. Eng. Technol., vol. 7, no. 3, p. 1372, Jul. 2018.
T. Mustaqim, K. Umam, and 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, pp. 8–15, 2020.
S. Kaur, G. Sikka, and L. K. Awasthi, “Sentiment Analysis Approach Based on N-gram and KNN Classifier,” ICSCCC 2018 - 1st Int. Conf. Secur. Cyber Comput. Commun., pp. 13–16, 2018.
S. A. Jafar Zaidi, I. Chatterjee, and S. Brahim Belhaouari, “COVID-19 Tweets Classification during Lockdown Period Using Machine Learning Classifiers,” Appl. Comput. Intell. Soft Comput., vol. 2022, pp. 1–8, Jul. 2022.
V. Kandasamy et al., “Sentimental analysis of covid-19 related messages in social networks by involving an n-gram stacked autoencoder integrated in an ensemble learning scheme,” Sensors, vol. 21, no. 22, 2021.
P. Sharma and T.-S. Moh, “Prediction of Indian election using sentiment analysis on Hindi Twitter,” in 2016 IEEE International Conference on Big Data (Big Data), 2016, pp. 1966–1971.
K. M. A. Hasan, M. S. Sabuj, and Z. Afrin, “Opinion mining using Naïve Bayes,” in 2015 IEEE International WIE Conference on Electrical and Computer Engineering, WIECON-ECE 2015, 2016, pp. 511–514.
B. Bhutani, N. Rastogi, P. Sehgal, and A. Purwar, “Fake News Detection Using Sentiment Analysis,” 2019 12th Int. Conf. Contemp. Comput. IC3 2019, pp. 1–5, 2019.
K. Poddar, G. B. D. Amali, and K. S. Umadevi, “Comparison of Various Machine Learning Models for Accurate Detection of Fake News,” 2019 Innov. Power Adv. Comput. Technol. i-PACT 2019, pp. 1–5, 2019.
DOI: https://doi.org/10.33633/jais.v7i2.6734
Article Metrics
Abstract view : 371 timesPDF - 196 times
Refbacks
- There are currently no refbacks.
Journal of Applied Intelligent System (e-ISSN : 2502-9401, p-ISSN : 2503-0493) is published by Department of Informatics Universitas Dian Nuswantoro Semarang and IndoCEISS.
Journal of Applied Intelligent System indexed by :
This journal is under licensed of Creative Commons Attribution 4.0 International License.