Analysis of Public Sentiment Toward Government Digitalization Using NLP and Deep Learning in Python

Abstract

This study aims to analyze public sentiment toward the Indonesian government’s digitalization policies on the X social media platform using Natural Language Processing (NLP) and Python-based deep learning approaches. The research data consist of posts related to the Electronic-Based Government System (SPBE), One Data Indonesia, and Digital Identity policies collected during 2025–2026. The research stages include data collection, text preprocessing, non-organic account filtering, sentiment labeling, model development using deep learning algorithms (LSTM, CNN, and IndoBERT/IndoBERTweet), and performance evaluation using accuracy, precision, recall, and F1-score metrics. Furthermore, topic analysis is conducted to identify policy issues that generate the highest proportion of negative sentiment. The expected outcomes are an accurate sentiment classification model and a comprehensive understanding of public perceptions of governmental digital transformation. The findings are anticipated to provide a foundation for government agencies to formulate communication strategies and improve digital public services in a manner that is more responsive to citizens’ needs.