Detection the topics of Facebook posts using text mining with Latent Dirichlet Allocation (LDA) algorithm - Shahlaa Mashhadani (Ibn AL-Haitham Journal For Pure and Applied Sciences, 2025)
The development of artificial intelligence technologies has led to their massive integration in various fields, including daily life. Text data plays a pivotal role in the world of artificial intelligence, especially in machine learning, allowing valuable insights to be extracted from massive data sets to help make informed decisions. Latent Dirichlet Allocation (LDA) and digital forensics intersect through analyzing and classifying textual digital evidence in social media, including Facebook, in which text data is the main focus. This technique is particularly a useful topic modeling technique for uncovering hidden patterns in text data, which can be particularly useful in digital forensics taken from Facebook, including text analysis and evidence discovery, where LDA is used to extract large amounts of unstructured text data from meaningful topics, such as emails, documents, or chat logs. Investigators often deal with huge amounts of text-based evidence, so this technique helps them identify topics, such as fraud, especially in relation to text data, which is the core of our research. It not only improves effort and time but also carries a huge potential for security packages. This work presents a method for processing Facebook posts with the help of a Latent Dirichlet Allocation (LDA) ruleset to classify these texts into coherent themes. The significance of the research lies in its ability to discover themes within each post, which is crucial for analyzing user behavior and addressing security concerns. The use of relevant Facebook data enhances the real-world relevance of the results, facilitating targeted analysis based on the language patterns used by users in these posts and thus contributing to the success of security objectives. In evaluating existing methodologies, this study demonstrates improved performance by optimizing the LDA ruleset to more accurately match the unique features of the target statistics. This improvement leads to improved performance and reduced errors. The results of this study demonstrate the effectiveness of using the LDA approach, as it showed significant improvements over traditional strategies in terms of accuracy and applicability to real-world security situations and digital analytics.