Aspect-based Sentiment Analysis and Topic Modelling of International Media on Indonesia Tourism Sector Recovery

Sita Aliya Rutba; Setia Pramana

doi:10.36256/ijtl.v6i1.502

Aspect-based Sentiment Analysis and Topic Modelling of International Media on Indonesia Tourism Sector Recovery

Sita Aliya Rutba, Setia Pramana

Abstract

The international world's perception of a country is essential. In 2022, Indonesia attracted international media attention, which can form the nation’s image of Indonesia in the international public perception. Therefore, this study analyses international media, online news, and social media Twitter perceptions towards Indonesia. For the news, aspect-based sentiment analysis is carried out, and for Twitter opinion, sentiment classification using multiple classification algorithms, and topic modeling related to these sentiments are carried out. Furthermore, this research classifies news sentiment based on aspects of forming the country's image, such as tourism, exports, diplomacy, government policies, and people's behavior. It was obtained that aspects of people, policy, and tourism, besides being classified as “none” class, mostly classified in negative sentiment. While diplomacy and export are mostly positive sentiment. One limitation of this classifier is the insufficient number of cases in the training data, which led to relatively low accuracy and precision in this study. On Twitter opinion, it was found that Twitter's positive sentiment about Indonesia is associated with tourism recovery. The topic modeling of positive tweets highlighted international interest in Indonesia's tourism. This study's findings can provide valuable insights for the government on boosting foreign tourism to support economic growth. Additionally, policymakers should focus on addressing issues that attract foreign media attention. By effectively managing these concerns, Indonesia’s branding can be enhanced, potentially leading to an increase in tourist arrivals.

Keywords

International Media; Nation’s Image; Sentiment Analysis; Topic Modelling; Tourism Recovery

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DOI: https://doi.org/10.36256/ijtl.v6i1.502