Implementation of Latent Dirichlet Allocation Topic Modeling and VADER on Aspect-Based Sentiment Analysis - Jatmika; Kevin Miracle Satoko Kevin; Sunneng Sandino Berutu; Retno Palupi (Universitas Kristen Immanuel, 2026)
Aspect-Based Sentiment Analysis on a Product or Service is Crucial for Enhancing Customer Satisfaction. This Study Applies Latent Dirichlet Allocation (LDA) Topic Modeling to Identify Aspects. Then, the Valence Aware Dictionary and Sentiment Reasoner (VADER) Lexicon Method is Adopted to Determine Sentiment on These Aspects. The Data Source Comes from Customer Reviews of a Gelato Ice Cream Shop at Taman Siswa. Data was collected from Google Maps Using the Web Scraping Method via the Instant Data Scrapper Application. The Experimental Results Show that the LDA Method Identified 3 Aspects: Flavor, Place, and Service. Meanwhile, Sentiment Measurement Using VADER on the Flavor Aspect Revealed a Positive Sentiment of 213%, Negative Sentiment of 60%, and Neutral Sentiment of 218%. The Next Aspect, Place, Had a Positive Sentiment of 32%, Negative Sentiment of 4%, and Neutral Sentiment of 4%, while the Service Aspect Composed of 32% Positive Sentiment, 3% Negative Sentiment, and 3% Neutral Sentiment. Overall, the Positive Sentiment for the Flavor Aspect (Taste) Outweighed the Negative and Neutral Sentiments for the Place (Location) and Service (Service) Aspects.