Twitter Topic Modelling Using Latent Dirichlet Allocation Approach

Uce  Indahyanti; Yulian Findawati  Findawati; Endah  Asmawati; Achmad  Ariansyah

Authors

Uce Indahyanti
uceindahyanti@umsida.ac.id
Faculty of Science and Technology, Universitas Muhammadiyah Sidoarjo, Sidoarjo.
Yulian Findawati Findawati Faculty of Science and Technology, Universitas Muhammadiyah Sidoarjo, Sidoarjo.
Endah Asmawati Faculty of Engineering, Universitas Surabaya, Surabaya.
Achmad Ariansyah Faculty of Science and Technology, Universitas Muhammadiyah Sidoarjo.

Vol. 4 No. 2 (2024): APRIL

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May 26, 2024

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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.

Indahyanti, U. ., Findawati , Y. F. ., Asmawati, E. ., & Ariansyah, A. . (2024). Twitter Topic Modelling Using Latent Dirichlet Allocation Approach. International Journal of Innovative Analyses and Emerging Technology, 4(2), 8–14. Retrieved from https://oajournals.net/index.php/ijiaet/article/view/2639

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International Journal of Innovative Analyses and Emerging Technology

Twitter Topic Modelling Using Latent Dirichlet Allocation Approach

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