## 2 Related Work
#### Topic Modeling Comparative Studies Using Arabic Data Sets  
Despite the advancement of NLP studies on Arabic language data sets, the practical deployment of topic modeling for Arabic remains somewhat limited. This section delves into a selection of recent research, specifically concentrating on comparing topic modeling methods within the context of Arabic linguistics. In their research, [PERSON], [PERSON], [PERSON], and [PERSON] [15] conducted a comprehensive analysis comparing the efficacy of six topic modeling algorithms applied to a data set of Arabic newspaper articles. They established benchmarks using LDA and NMF models before comparing these with advanced neural topic models, such as the Contextualized Topic Model (CTM), Embedded Topic Model (ETM), AraBERT, and RoBERTa. Their evaluation metrics included topic coherence, diversity, and computational efficiency. Remarkably, they found that the neural BERTopic framework, utilizing a RoBERTa-based sentence transformer, excelled in coherence, achieving a score 36% higher than BERTopic integrated with AraBERT, albeit with a 6% lower topic diversity compared to the CTM model and necessitating double the computational time. Their research also highlighted a pre-processing limitation, where the exclusive use of 1-gram tokens and noun retention posed challenges for neural models dependent on the corpus vocabulary size and document length. Addressing future research, the authors suggested broadening pre-processing approaches through including a wider array of token types and delving into \(n\)-gram models beyond \(n=1\). This would involve evaluating the impacts of different pre-processing strategies on neural model performance and customizing the steps to the specific needs of the model. They advocated for thoroughly evaluating these pre-processing modifications across various data sets to confirm the robustness and applicability of the findings, aiming to enhance the precision and relevance of topic extraction from Arabic texts and mitigate the initial study's pre-processing limitations. The study by [PERSON] and [PERSON] [16] presented a thorough examination of the effectiveness of BERTopic, utilizing LDA and NMF performances as baselines for comparison. Employing a data set of 111,728 documents from three online newspapers, categorized into five distinct classes, their research aimed to measure topic coherence using NPMI metrics. The study distinguished itself by leveraging various pre-trained language models, including monolingual Arabic BERT models (AraBERTV2.0, ARBERT, and QARiB) and a multilingual BERT model (XLM-R), demonstrating the superior accuracy and coherence of BERTopic in topic extraction compared to traditional methods. However, they fell short of providing comprehensive methodological details, such as the selection of \(n\)-grams and specific parameter settings for each algorithm. These omissions are pivotal for ensuring the study's reproducibility and for a full understanding of the basis on which the algorithms were evaluated. Furthermore, while the use of topic coherence as a primary evaluation metric is standard and valuable, the study's exclusive reliance on it without incorporating a wider array of metrics (e.g., perplexity and topic diversity) potentially narrowed the assessment of the algorithms' performance. Broadening the evaluation criteria to include multiple metrics would furnish a more nuanced and thorough analysis of each algorithm's capabilities. [PERSON] et al. (2023), in their study titled \"ChatGPT across Arabic Twitter: A Study of Topics, Sentiments, and Sarcasm\" [17], explored the thematic landscape, sentiment orientation, and sarcasm detection among Arabic-speaking Twitter users interacting with ChatGPT. Analyzing a corpus of 34,760 tweets, the researchers identified predominant discussions centered around the technological insights, ethical concerns, and regional applications of ChatGPT.