The Development of Social Media Intelligence System for Citizen Opinion and Perception Analysis over Government Policy

Muhammad Habibi, Muhammad Rifqi Ma'arif, Dayat Subekti

Abstract


In Indonesia, community involvement in development planning and public policy has generally been carried out but limitedly. Social media uploads regarding public perceptions of policy implementation in the field are valuable input for those who quickly and accurately upload existing problems.

The problems that arise from this effort to use social media are 1) how to detect public conversations related to a public policy. 2) Social media data collected extensively and accelerating can be processed quickly to get real-time analysis results. 3) Making the analysis results accessible in an interactive and representative form allows government policymakers to explore appropriate data and information to formulate and formulate public policies.

This research produces a social media intelligence platform that can unite public opinion regarding public perceptions of the implementation of policies issued by the government, especially local governments in Indonesia. Based on modeling the topic of Covid-19 vaccination cases, 11 topics of discussion were obtained. While the sentiment analysis results of the 11 issues resulted, topic 6 had the most negative sentiment values regarding the development of Covid-19 vaccination in Indonesia. At the same time, topics with the most positive sentiment values are topic three and topic 10. These topics discuss the vaccination process carried out by health procedures (prokes) and government policies related to COVID-19 vaccination.

Keywords


Sentiment Analysis; Topic Modeling; Machine Learning

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DOI: https://doi.org/10.31315/telematika.v19i1.6447

DOI (PDF): https://doi.org/10.31315/telematika.v19i1.6447.g4418

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