Aspect-Based Sentiment Analysis for Enhanced Understanding of 'Kemenkeu' Tweets
Abstract
The perceptions and expressions shared by the public on social media play a crucial role in shaping the reputation of government institutions, such as the Ministry of Finance MOF (Kemenkeu) in Indonesia which also has faced increased scrutiny, particularly on Twitter. This study analyzes public sentiment towards the Indonesian Ministry of Finance (MoF) through Aspect-Based Sentiment Analysis (ABSA) on Twitter data. Using a dataset of 10,099 tweets from January to July 2024, this study combines IndoBERT for sentiment classification and Latent Dirichlet Allocation (LDA) for topic modeling. Here, LDA was tested across four scenarios that considered various combinations of stopwords removal and stemming techniques, resulting in coherence scores of 0.314256, 0.369636, 0.350285, and 0.541752. The most optimal results were achieved in the scenario of stopwords removal without stemming (with 0.314256 coherence score). The main results show: 1) Identification of four main topics related to MoF: Economy, Budget, Employees, and Tax; 2) The dominance of negative sentiment (6,837 tweets) compared to positive sentiment (198 tweets) across all topics; 3) The effectiveness of IndoBERT in handling the complexity of the Indonesian language, especially in interpreting context and language nuances; 4) The importance of proper preprocessing, with a scenario of removing stopwords without stemming resulting in the most relevant topics. This study provides valuable insights for MoF to understand public perception and identify areas that require special attention in public communication and policy.
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