BERTopic Analysis of Indonesian Biodiversity Policy on Social Media
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Abstract
Indonesia, known for its diverse biodiversity, faces critical challenges such as habitat degradation and species loss. This study delves into public opinion regarding Indonesian government biodiversity policies by analyzing text data from X social media platforms. Leveraging BERTopic, an advanced topic modeling technique, we uncover nuanced topics related to biodiversity within tweets. Our research uniquely contributes by exploring diverse combinations of BERTopic parameters on Indonesian text, assessing their efficacy through coherence values and manual content evaluation. Notably, our findings highlight the optimal combination of sentence embedding, cluster model, and dimension reduction parameters, with Model 5 demonstrating the highest coherence score of 0.7733. Moreover, we elucidate the impact of outlier reduction techniques when applying BERTopic in an Indonesian context. Our study serves as a foundational model for categorizing Indonesian-language topics using BERTopic, showcasing the significance of tailored text processing techniques. We also reveal that while standard preprocessing methods enhance clustering outcomes, certain dataset characteristics, such as the inclusion of hashtags and mentions, can inuence coherence differently across models. This work not only provides insights into public perceptions of biodiversity policies but also offers methodological guidance for text analysis in similar contexts.
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