Classification of Sentiment Analysis and Community Opinion Modeling Topics for Application of ICT in Government Operations

Abstract

Utilizing information systems is very useful in the current era. Digitizing administration in the Village is beneficial in the service process to the public. This is seen as a change in service that can make it easier or more difficult for the people of Sanrobone Village to take care of administration at the village office. This study aims to analyze public opinion regarding the use of e-government, predict public opinion regarding the use of e-government, and analyze modeling topics related to the use of e-government. This research applies a text mining algorithm with a sentiment analysis method to see positive, negative, and neutral public perceptions and also uses topic modeling to get the most frequently appearing topics in the data. Stages in this study include Data Collection, Text Pre-processing, Sentiment Analysis, Topic Modelling, Classification, and Evaluation. The results obtained are the ten words that appear most often in the responses of the Village community: easy 122, help 96, village 80, accessed 80, letter 80, permit 77, resident 73, manage 60, service 52, and the person with 52 words. The sentiment analysis is positive, with 411 opinions, 37 negative opinions, and 152 neutral opinions. Finally, the performance of the Nave Bayes algorithm in predicting classification results is excellent, with an accuracy rate of 98 percent.

Deep Learning-Driven Public Opinion Analysis on the Weibo Topic about AI Art

“…The structure of the SRU is depicted in Figure 5. In contrast to LSTM, SRU utilizes only the forget gate 𝑓 to compute the current cell status 𝑐 , as illustrated in Equations ( 7) and (8). In contrast to LSTM, SRU utilizes only the forget gate f t to compute the current cell status c t , as illustrated in Equations ( 7) and (8).…”

“…They used the SVM classifier to categorize tweets into fine-grained sentiments, and a word cloud was utilized to provide a comprehensive view of people's attitudes, providing public opinion references to the authorities for the revision and promotion of related policies. To explore the public's perception of the utilization of ICT in government operations, Risal et al [8] utilized Naïve Bayes to conduct sentiment analysis of related tweets, followed by topic modeling using LDA. Their results provide useful insights into the usage and development of e-government.…”