Analysing Maritime Piracy Reports Using a Text Mining–Based Frequency and Thematic Approach
DOI:
https://doi.org/10.5281/zenodo.20297788Keywords:
Text Mining, Maritime Piracy, Porter Stemming, Maritime Security, Incident AnalysisAbstract
Maritime piracy poses persistent risks to crew safety, vessel operations, and global trade efficiency. To identify dominant patterns and trends in piracy incidents, this study applies a text mining-based analysis to maritime piracy reports published between 2021 and 2023 by the International Maritime Bureau’s (IMB) Piracy Reporting Centre. The Porter Stemming algorithm is used to preprocess unstructured textual data and extract high-frequency terms related to piracy events. Based on word frequency distributions and semantic similarity, recurring terms are grouped into thematic categories representing attack characteristics, locations, operational methods, and incident timelines. The results indicate that piracy reports primarily emphasise attack locations, threat execution, vessel boarding actions, and the temporal progression of incidents, while post-incident response measures receive comparatively less attention. The study demonstrates that a purely data-driven text mining approach can effectively reveal structural patterns in maritime piracy reports and support situational awareness without relying on expert judgement or multi-criteria decision-making models.
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