Articles
| Open Access |
ANALYZING AND CLASSIFYING COMMUNITY COMPLAINTS AGAINST PUBLIC SERVICES ON TWITTER
Siti Muslihah , Institute Teknologi Bisnis Aas Indonesia Surakarta, IndonesiaAbstract
This study presents an in-depth analysis of community complaints directed at public services on Twitter. With the rapid growth of social media as a platform for public discourse, understanding and categorizing these complaints is crucial for governments and service providers to improve responsiveness and service quality. Leveraging natural language processing techniques, sentiment analysis, and machine learning, we examine a diverse range of Twitter complaints, classify them into specific categories, and provide insights into the most common issues faced by communities when interacting with public services on this platform. The findings offer valuable guidance for enhancing public service delivery and engagement in the digital age.
Keywords
Twitter, Community Complaints, Public Services
References
B. Kurniawan, M. A. Fauzi, and A. W. Widodo, “Twitter News ClassificationUsed Metode Improved Naïve Bayes,” vol. 1, no. 10, pp. 1193–1200, 2017.
K. Yu, “Toward an Incremental Democracy and Governance : Chinese Theories and Assessment Criteria,” New Polit. Sci., vol. 24, no. 2, pp. 181–199, 2002.
V. Effendy, A. Novantirani, and M. K. Sabariah, “Sentiment Analysis on Twitter about the Use of City Public Transportation Using Support Vector Machine Method,” Intl. J. ICT, vol. 2, no. 1, pp. 57–66, 2016.
Rane and A. Kumar, “Sentiment Classification System of Twitter Data for US Airline Service Analysis,” Proc. -Int. Comput. Softw. Appl. Conf., vol. 1, pp. 769–773, 2018.
M. Qamar, S. A. Alsuhibany, and S. S. Ahmed, “Sentiment Classification of Twitter Data Belonging to Saudi Arabian Telecommunication Companies,” Int. J. Adv. Comput. Sci. Appl., vol. 8, no. 1, pp. 395–401, 2017.
X. Li, Z. Ma, and H. Chen, “QODM: A query-oriented data modeling approach for NoSQL databases,” in Proceedings - 2014 IEEE Workshop on Advanced Research and Technology in Industry Applications, WARTIA 2014, 2014, pp. 338–345.
S. K. Kim, M. J. Park, and J. J. Rho, “Effect of the Government ’ s Use of Social Media on the Reliability of the Government : Focus on Twitter,” no. April 2015, pp. 37–41, 2015.
L. Jiang, M. Yu, M. Zhou, X. Liu, and T. Zhao, “Target- dependent Twitter Sentiment Classification,” pp. 151–160, 2011.
Article Statistics
Downloads
Copyright License
Copyright (c) 2023 Siti Muslihah
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright and Ethics:
- Authors are responsible for obtaining permission to use any copyrighted materials included in their manuscript.
- Authors are also responsible for ensuring that their research was conducted in an ethical manner and in compliance with institutional and national guidelines for the care and use of animals or human subjects.
- By submitting a manuscript to International Journal of Computer Science & Information System (IJCSIS), authors agree to transfer copyright to the journal if the manuscript is accepted for publication.