A Lexicon-Based VADER Approach for Aspect-Based Sentiment Analysis in the Indonesian Language
Abstract
Aspect-Based Sentiment Analysis (ABSA) provides detailed insights into customer opinions by identifying specific aspects—such as product, service, and management—in textual reviews and analyzing the sentiment toward each aspect. Unlike general sentiment analysis, ABSA reveals which dimensions of customer experience require improvement. However, applying ABSA in low-resource languages like Indonesian is challenging due to limited annotated dataset, sentiment lexicon, and pre-trained model, which often reduce the accuracy of machine learning or deep learning approaches. This study employs the Valence Aware Dictionary for Sentiment Reasoning (VADER), a lexicon-based algorithm effective in analyzing short, informal, and mixed-language texts, such as online reviews. VADER enables reliable sentiment scoring without large labeled datasets, making it suitable for Indonesian-language analysis. A total of 8,438 Google Maps reviews from 2016 to 2025 were analyzed to observe sentiment trends over time. Keywords were developed for three main aspects: product (1,112 words), service (468 words), and management (666 words). Results show that most reviews express positive sentiment (85.4%), followed by neutral (9.9%) and negative (4.6%). The product aspect was most discussed (7,839 reviews), followed by management (4,608) and service (4,589). In conclusion, VADER-based ABSA can effectively analyze customer sentiment in low-resource languages, providing actionable insights to guide restaurant service improvements. The lack of VADER are an obstacle in handling nuance in the Indonesian language and many keywords cannot be extracted by VADER. Futher, method development is needed for more precise aspect extraction.
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