The Utilization of CAMeL Tools in the Part-of-Speech Analysis of Arabic in the Short Story Abdullah wa al-Ushfur - Khairina Nasution; Rima Mella; Rozanna Mulyani (CV. Rustam, 2026)
This study aims to analyze Arabic word categories, namely ism, fi'il, and harf, in the short story Abdullah wa al-Ushfur using CAMeL Tools. The study employed a qualitative descriptive approach assisted by Natural Language Processing (NLP) technology. The data consisted of the text of Abdullah wa al-Ushfur, obtained from the muthala'ah materials of the Kulliyatul Mu'allimin al-Islamiyah (KMI) second grade. The analysis was conducted using the Part-of-Speech (POS) Tagging feature in CAMeL Tools to automatically identify word classes based on their grammatical functions. The findings revealed that CAMeL Tools successfully identified various word classes, which were subsequently classified into the three primary categories of Arabic grammar: ism, fi'il, and harf. The category of ism was frequently used to refer to characters, objects, places, and conditions within the story, while fi'il contributed to the development of the narrative through the characters' actions. Meanwhile, harf functioned as a linking element that maintained the coherence of sentence structures. The findings suggest that CAMeL Tools can support Arabic linguistic analysis in a more systematic and efficient manner. This study is expected to contribute to technology-based Arabic linguistic studies and serve as a reference for future research integrating NLP into the analysis of Arabic literary texts.