Twitter Topic Modelling Using Latent Dirichlet Allocation Approach
Keywords:
Topic Modeling, Twitter Data, the Kanjuruhan Tragedy, LDA, Text MiningAbstract
The Kanjuruhan tragedy, a fatal incident following a football match at Kanjuruhan Stadium in Malang, Indonesia, became a trending topic on Twitter. This study applies Latent Dirichlet Allocation (LDA) to analyze 1480 Indonesian-language tweets about the tragedy, aiming to identify underlying patterns and main topics within the discourse. The analysis revealed five primary topics: the PSSI (Indonesian Football Association) investigation, suspects, the Itaewon tragedy, Korean netizens (Knetz), and the use of tear gas. These findings provide insights into public reactions and expectations, offering valuable information for stakeholders to consider in response to the incident and for future policy-making.
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