## 5 Conclusions  
This study comprehensively explored the impacts of various pre-processing strategies on the effectiveness of BER Topic in modeling topics in Arabic texts, comparing it with the traditional methods LDA and NMF. The methodologies employed, the selection of the SANAD data set, and the detailed experimental setup provided a framework for evaluating the efficacy of these topic modeling techniques. The comprehensive evaluation of the three models highlighted the significance of pre-processing techniques, particularly Tashaphyne stemming, in enhancing the performance of traditional topic modeling algorithms (i.e., LDA and NMF). For BER Topic, the combination of cleaning, employing 2-grams, and using the CAMeL-Lab/bert-base-arabic-camelbert-da embedding yielded the best NPMI score. This optimized approach achieved superior results when compared to previous studies conducted by [PERSON], [PERSON], [PERSON], and [PERSON] [24], as well as [PERSON] and [PERSON] [16], both of which used similar Arabic news article data sets [29]. Specifically, this study achieved an NPMI score of 0.35, compared with their scores of 0.11 and 0.17, respectively, which translates to a 9% enhancement in topic coherence. In conclusion, this research demonstrated that BER Topic, with effective pre-processing and fine-tuning, is a powerful tool for the topic modeling in Arabic texts. The insights gained from comparing BER Topic with traditional methods provide a valuable benchmark for future studies. Future research could explore the integration of large language models, such as Llama, for topic representation. This could enhance the richness and accuracy of topic models, leading to more sophisticated topic modeling applications. However, this study has certain limitations. Firstly, Arabic is a highly diglossic language, with Modern Standard Arabic (MSA) used in formal settings and various regional dialects in informal contexts. The data set used in this study focused solely on MSA, meaning that the findings may not generalize to Arabic dialects, which exhibit notable syntactic, lexical, and morphological differences. Secondly, the field lacks well-established benchmarks and evaluation metrics specifically tailored for Arabic topic modeling, making it challenging to evaluate model performance consistently and compare results across studies. Further research is needed to establish Arabic-specific benchmarks that accurately reflect topic coherence and interpretability. Lastly, topic modeling in Arabic remains a relatively under-researched field. While progress has been made, many methodologies and optimizations developed for other languages may not directly transfer to Arabic due to its unique linguistic characteristics. This limitation restricts the availability of best practices and comparative studies that could guide researchers in selecting the most effective approaches for Arabic topic modeling. Figure 2: Evaluation and optimization results. **Author Contributions:** Conceptualization, H.A. and N.A. ; methodology, H.A.and N.A; software, H.A. ; validation, H.A. and N.A. ; formal analysis, H.A and N.A. ; investigation, H.A. and N.A. ; resources, H.A. and N.A; data curation, H.A. ; writing--original draft preparation, H.A. and N.A. ; writing--review and editing, H.A. and N.A. ; visualization, H.A. and N.A. ; supervision, N.A. ; project administration, H.A. and N.A. ; funding acquisition, N.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding. **Informed Consent Statement:** Not applicable.