The Role of Twitter Sentiment Analysis for Disaster Resilience in a World Class Destination: A Case Study of Ubud, Bali
DOI:
https://doi.org/10.37680/jcd.v7i2.8421Keywords:
Sentiment Analysis, Natural Language Processing, Twitter, Ubud, Disaster Resilience, TourismAbstract
Accurate, real-time information is crucial for Disaster Resilience efforts, making it essential for prominent international tourist destinations in Bali, such as Ubud, which are vulnerable to hydro-meteorological hazards. This study aims to explore public response patterns to the landslide and flood disaster in Ubud via Twitter (X) data, interpreting the resulting sentiment as a real-time social indicator for crisis management. The methodology adopts the CRISP-DM framework, utilizing a Natural Language Processing (NLP) approach—specifically lexicon-based sentiment analysis and visualization—on Indonesian-language tweet data with the keywords ubud banjir (Ubud flood) and prayforbali following the disaster in October 2022. Key findings reveal an absolute dominance of positive polarity (reaching 98.8%) in public opinion. This significantly high positive sentiment is interpreted as a digital manifestation of strong Social Capital and a collective priority to safeguard Ubud's destination image during the recovery phase, which contrasts with sentiment patterns observed in non-tourism disaster contexts. This analysis contributes to Disaster Informatics by positioning public sentiment as a Real-time Social Indicator. A recommendation that local governments leverage this sentiment data as a strategic asset to enhance crisis communication transparency and reinforce Ubud’s image as a resilient and safe international destination.
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