## 3 Methodology
### Modeling  
The following three topic modeling techniques were evaluated:  
* LDA: A probabilistic model that identifies topics based on word distributions within documents. It aims to infer these concealed topics by examining visible words [24]. Prior research has indicated the efficacy and utility of LDA methods in the field of topic modeling [25]. The LDA model was implemented using the Gensim library, with a structured approach to data preparation and model training. Initially, documents were tokenized into lists of words, and a dictionary was created using the gensim.corpora.Dictionary class to map words to unique integer IDs. This dictionary was then used to generate a bag-of-words representation of the documents, which served as the model's input. For training, the model parameters were configured as follows: the number of topics (num_topics) was set to 7 based on iterative optimization for the data set, while workers were set to 4 to enable efficient parallel computation. The corpus parameter utilized the bag-of-words representation, and id2 word was assigned to the dictionary mapping of IDs to words.
* NMF: A linear algebraic model that utilizes statistical techniques to identify thematic structures within a collection of texts. It employs a decompositional strategy based on matrix factorization, categorizing it within the realm of linear algebraic methods. It stands out as an unsupervised approach for simplifying the complexity of non-negative matrices. Additionally, when it comes to mining topics from brief texts, learning models based on NMF have proven to be a potent alternative to those predicated on LDA [26]. The NMF model was implemented using the Gensim library, with a focus on consistent data preparation and training parameters. The text data were tokenized and transformed into a bag-of-words format using the gensim.corpora.Dictionary class. For model training, the number of topics (num_topics) was set to 7 to ensure consistencywith the LDA and BERTopic models. The corpus parameter utilized the bag-of-words representation, and id2 word was assigned to the dictionary mapping of IDs to words.
* BERTopic: A method for topic modeling that employs an unsupervised clustering approach. It leverages Bidirectional Encoder Representations from Transformers (BERT) to generate the contextual embeddings of sentences. These embeddings encapsulate the semantic details of sentences, enabling the algorithm to identify topics through their contextual significance. The methodology of BERTopic encompasses a three-stage process. Initially, it transforms documents into embedding representations utilizing a pre-trained language model. Subsequently, it diminishes the dimensionality of these embeddings to facilitate more effective clustering. In the final stage, BERTopic derives topic representations from the clusters of documents through applying a unique class-based variant of TF-IDF, a technique detailed by [PERSON] in 2022 [9]. A key strength of BERTopic lies in its precision in forming clusters and its capability to propose names for topics based on these clusters. Unlike traditional topic modeling approaches, BERTopic does not require the number of topics to be pre-defined, offering a flexible and dynamic framework for topic discovery [27]. The BERTopic model utilized pre-trained transformer models tailored specifically for Arabic text analysis, enabling the generation of robust semantic embeddings. The textual data were transformed into high-dimensional vector representations using a variety of pre-trained models. During training, the dimensionality of the embeddings was reduced to enhance clustering efficiency. To ensure consistency with LDA and NMF, the reduce_topics method was employed, standardizing the number of topics to 7 for direct comparison across models.