Abstract
The mangrove ecosystem is crucial for addressing climate change and supporting marine life. To preserve this ecosystem, understanding community awareness is essential. While latent Dirichlet allocation (LDA) is commonly used for this, it has drawbacks such as high resource requirements and an inability to capture semantic nuances. We propose a technique using Sentence-BERT and K-Means Clustering for topic identification, addressing these drawbacks. Analyzing mangrove-related Twitter data in Indonesian from 1 September 2021 to 31 August 2022 revealed nine topics. The visualized tweet frequency indicates a growing public awareness of the mangrove ecosystem, showcasing collaborative efforts between the government and society. Our method proves effective and can be extended to other domains.
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