Sentiment Analysis of Technology Utilization by Pekanbaru City Government Based on Community Interaction in Social Media

Abstract

Government services for the public are currently utilizing technology, especially in the city of Pekanbaru. The government has currently centralized all services for the public, both online and offline, in public service malls. The type of service that uses technology, especially for online services, has received criticism in online media such as Twitter. To see the public's response to Pekanbaru city government services, especially in terms of technology, this study will use sentiment analysis to see positive, negative, and neutral comments. The method used is to see the accuracy generated using the Naïve Bayes Classifier (NBC) method. Bayes classifier is a statistical classifier, where the classifier can predict the probability of class membership of a data tuple that will fall into a certain class, according to the probability calculation. Accuracy results are obtained by dividing training data and testing data with a comparison of 70%:30% with an accuracy value of 55.56%, Precision 64%, recall 80%, f-score 71.2%.

Application of the Naive Bayes Algorithm in Twitter Sentiment Analysis of 2024 Vice Presidential Candidate Gibran Rakabuming Raka using Rapidminer

“…These studies have delved into various applications of sentiment analysis, ranging from analyzing sentiments toward COVID-19 vaccines [23], traffic risk management [33], hotel reviews [34], public trust in government policies during the pandemic [35], to sentiments related to the COVID-19 booster vaccine [36]. Moreover, sentiment analysis has been conducted on a wide array of subjects, including sentiments towards airlines [37], academic articles [38], Indonesian general analysis datasets [39], Bali tourism during the pandemic [40], internet service providers [41], work from home policies [42], and technology utilization by local governments [43]. These studies have used algorithms like Naïve Bayes and Support Vector Machine (SVM) to compare public responses and categorize sentiments into positive, negative, and neutral classes [44].…”