## 1 Introduction  
Collision accidents in a maritime environment primarily occur due to environmental factors, human error, and mechanical failures [1, 2]. When an accident occurs, investigations are conducted to prevent re-occurrences because maritime collision accidents usually lead to casualties, environmental damages, and economic damages [3]. In South Korea, the Korea Maritime Safety Tribunal (KMST) dispatches investigators to the scene to conduct a survey and to accurately identify the causes of the accident [4]. Information obtained from the investigation must be contained in the verdict and categorized based on the type of accident. However, when one takes a careful look at the verdicts, some cases are not merely collision accident cases but involve other types of maritime accidents. One collision case resulted in damages only to the ship's hull from a crash, but another case involved fires and explosions, crew deaths, and capsized ships. Nevertheless, both accidents were statistically categorized as \"collision\" accidents regardless of whether the accident was complex. The complex nature of maritime collision accidents is however apparent because the maritime environment itself is exposed to various hazards, many of which can lead to accidents [5, 6], but since complex maritime collision accidents lead to worse consequences than non-complex ones, latent causes have to be identified to prevent future complex maritime collision accidents. This study uses textual data. Text analytics in other fields of research and industries have usually concentrated on practical purposes [7, 8, 9] such as semantic analysis of customer opinions about products, improving product quality and the efficiency of workflows [10, 11],or visualizing the interaction and connection between text information [12]. Accordingly, this paper used a large corpus of text data to extract certain underlying characteristics of texts, but unlike traditional text analytics, we extracted the trends in the use of words. Words that appear in a document with a certain purpose tend to reflect the concept conveyed by that document [13; 14]. In the case of cultural articles, these conceptual words are already marked as cultural words, including words found in these types of articles and words labeled as \"cultural\" [15]. In this paper, this characteristic of a text is called \"propensity\". The word propensity for accident categories can reveal words pointing towards hidden causes in the verdicts. The categories for the \"major maritime accidents\" designated by KMBT are \"collision,\" \"fire explosion,\" \"capsizes,\" \"casualty,\" and \"sinking.\" In this paper, we used five major categories of accidents for the extraction of word propensity and applied the propensity data to verdicts of collision accidents to reveal the latent causes behind complex collision accidents. Various research studies on the prevention of maritime collision accidents have used different techniques such as HFACS [2], MDTC model [16], and fuzzy cognitive maps [17], for example. The results of these research studies included human factors, environmental factors, collision situations, and contributors of collisions. Other research studies also focused on an analysis of maritime collision accident causes and derived specific results that impacted the collision of ships. Still, the prevention of collision accidents is challenging because of the uncertainty about the causes of an accident that are uncovered and the possible hidden causes that are not explicitly highlighted [18]. However, it may be possible to prevent complex collision accidents. Since verdicts are written by different investigators who have various analytical perspectives and who follow the requirements of various investigation approaches [19], it is very difficult to perform latent-cause examination simply by reading the verdict. Therefore, we tried to use a data-driven approach using text analytics to reveal latent-cause words and their relevant contexts in verdicts about complex maritime collision accidents.