A rabic Topic Classification Corpus of the Nakba Short Stories (2025)

Abstract: In this paper, we enrich Arabic Natural Language Processing (NLP) resources by introducing the “Nakba Topic Classification Corpus (NTCC),” a novel annotated Arabic corpus derived from narratives about the Nakba. The NTCC comprises approximately 470 sentences extracted from eight short stories and captures the thematic depth of the Nakba narratives, providing insights into both historical and personal dimensions. The corpus was annotated in a two-step process. One third of the dataset was manually annotated, achieving an IAA of 87% (later resolved to 100%), while the rest was annotated using a rule-based system based on thematic patterns. This approach ensures consistency and reproducibility, enhancing the corpus’s reliability for NLP research. The NTCC contributes to the preservation of the Palestinian cultural heritage while addressing key challenges in Arabic NLP, such as data scarcity and linguistic complexity. By like topic modeling and classification tasks, the NTCC offers a valuable resource for advancing Arabic NLP research and fostering a deeper understanding of the Nakba narratives

## introduction automatic document categorization has gained significant importance due to the continuous influx of textual documents on the web. the rise of the internet and web 2.0 has led to an unprecedented increase in unstructured data generated from various sources, particularly social media. this vast array of unstructured information presents both a challenge and an opportunity for data processing and management, enabling researchers to extract meaningful insights. one of the key tasks in this realm is text classification, which has witnessed substantial advancements recently, particularly with the advent of machine learning (ml) techniques @xcite ). text categorization, often referred to interchangeably as text classification, involves predicting predefined categories or domains for a given document. this automated process can either identify the most relevant single category or multiple closely related categories. given the enormous volume of available documents online, manual classification is impractical, necessitating automated classifiers that transform unstructured text into machine-readable formats @xcite . while text categorization has been well-studied in several languages, including english, the arabic language remains underrepresented in this research area. despite arabic being the fourth most widely used language on the internet and the sixth official language recognized by the united nations @xcite , there are few studies focusing on arabic text classification @xcite . the scarcity of comprehensive and accessible arabic corpora presents significant obstacles for researchers. most existing datasets are small, lack predefined classes, or require extensive modifications before use. this limitation complicates the validation and comparison of proposed methods, hindering progress in arabic text categorization @xcite . in this paper, we introduce the ntcc, a new arabic dataset specifically designed for emotion detection in narratives surrounding the nakba-a pivotal event in palestinian history characterized by displacement and loss. these stories are crucial for preserving the historical events and documenting the suffering of the palestinian people since 1948. our dataset encompasses five distinct categories: (i) historical events and politics, (ii) emotions and spirituality, (iii) nature and daily life, (iv) homesickness and war/conflict, and (v) others. "others" is dedicated to sentences that do not belong to any of the defined categories. this structure aims to provide researchers with flexibility in annotation and a more nuanced understanding of the narratives. by releasing this dataset, which consists of approximately 470 sentences extracted from eight stories, we aim to facilitate the application of various nlp and machine learning related tasks. our arabic annotated corpus will pave the way for researchers in machine learning and nlp to conduct numerous studies, potentially leading to advancements in sentiment analysis, topic modeling, and other classification tasks. notable works in this area include the use of deep learning models for text classification, sentiment analysis using recurrent neural networks (rnns), and transformerbased architectures such as bert for enhanced context understanding. this work contributes to preserving palestinian heritage and the palestinian issue by documenting these experiences. our objective is to enhance the predictive capabilities for semantic analysis on future unseen data, while contributing to the growing body of research in arabic corpora and text classification. this paper is organized as follows. in section 2, we present prior and recent research on arabic topic classification. section 3 provides a comprehensive analysis of the data handling. section 4 summarizes the steps followed, and describes the proposed approach for corpus construction. in section 5, we conclude and point out ideas for future search. ## related work the construction and utilization of structured arabic corpora are essential for advancing arabic text classification and natural language processing (nlp). due to the unique challenges posed by arabic-including its complex morphology, rich dialectal variations, and limited open-source resources-the development of specialized corpora has become a focal point in recent studies. @xcite propose an approach to arabic topic classification using generative and au-toml techniques, demonstrating how the success of classification models is heavily dependent on high-quality, diverse datasets. this study underscores the necessity of large, labeled corpora that allow models to generalize effectively, addressing arabic's unique linguistic challenges. in a similar direction, the osac (open-source arabic corpora) initiative (saad and ashour, 2010) tackles resource scarcity by providing open-access corpora to improve classification and clustering. osac compiles a wide range of arabic texts, including social media and news articles, thereby supporting the development of more robust classification models. in line with osac, the ahmed and ahmed (2021) study on arabic news classification develops a specialized corpus that enhances machine learning algorithms for news categorization. it stresses the importance of balanced data across categories to mitigate classification biases. likewise, systematic reviews such as @xcite identify trends across arabic nlp resources, noting that existing corpora often suffer from domain specificity and dialectal homogeneity. these studies collectively advocate for more diversified corpora that cover both modern standard arabic (msa) and regional dialects to bolster model robustness across different language forms. addressing specific nlp challenges, al-sarayreh et al. (2023) discuss data augmentation and annotation techniques as solutions to enrich arabic corpora, especially in low-resource contexts. they suggest that creative approaches to annotation and corpus expansion are essential for capturing arabic's linguistic diversity. furthermore, @xcite emphasizes the importance of domainspecific corpora and advocates for expanding corpus types to represent the broader spectrum of arabic content. this work argues that larger and more varied datasets can yield substantial improvements in classification accuracy and task transferability. in summary, as arabic nlp research progresses, the development of specialized, open-source, and diverse arabic corpora remains critical. the aforementioned studies contribute to this goal by offering resources that not only enhance classification accuracy, but also address broader challenges related to language variation, resource availability, and domain-specific needs in arabic nlp. ## data handling we want to construct a new arabic topic classification corpus based on the nakba narratives. the raw data represents a set of nakba short stories that contains the narratives of nakba, e.g. the suffering, memories, ... etc. as they were told by refugees. the stories were taken from the nakba archive website foot_0 . ## corpus construction this section presents our approach to developing a topic-classification dataset derived from nakba narratives. given that arabic nlp research has historically received less attention than research on western languages @xcite , our work aims to enrich arabic nlp resources with this specialized nakba corpus. ## conclusion we presented the nakba topic classification corpus 2 , an arabic annotated dataset developed to support research in arabic nlp, particularly in topic classification and emotion detection. through careful preprocessing, annotation, and validation, we achieved good topic labels across five categories. this corpus is expected to bridge gaps in arabic nlp resources and provide a foundation for future applications, including sentiment analysis and other machine learning classification tasks. by preserving and categorizing nakba narratives, our work not only contributes to advancing arabic nlp, but also serves as a vital resource for preserving and analyzing cultural narratives, offering a more in-depth understanding of the nakba's historical and emotional dimensions. future research could expand the corpus by adding nakba stories from diverse regions, arabic dialects, and leveraging pre-trained models like gpt.