Topic Modelling of Web Pages with Latent Dirichlet Allocation Methods

Topic modelling with Latent Dirichlet Allocation (LDA) is a popular technique used in natural language processing to uncover hidden thematic structures within a collection of documents. When applied to web pages, LDA can help in identifying prevalent topics or themes across these pages.. This study delves into the utilization of Latent Dirichlet Allocation (LDA) methods to extract underlying topics within web pages, a fundamental pursuit in understanding the multifaceted landscape of online information. Web content analysis presents unique challenges owing to its diverse nature—comprising text, images, videos, and structured HTML elements—mandating rigorous preprocessing strategies to homogenize the data. By adapting the LDA model to accommodate these challenges, this research tackles the task of uncovering latent thematic structures prevalent across web content. Methodologically, the study explores parameter tuning and model adaptation to optimize LDA for web page analysis, navigating complexities such as varied content formats, noise, and inherent biases in web data. Addressing these intricacies involves parsing HTML, extracting meaningful textual information, and refining tokenization processes. Evaluating the fidelity and interpretability of discovered topics becomes pivotal, prompting the utilization of coherence scores, perplexity metrics, and human assessment to gauge the quality of generated topics. Additionally, this research confronts the dynamic nature of web content, proposing strategies like continuous model retraining and dynamic topic modeling to accommodate evolving trends and updates. Practical applications of the extracted topics span a spectrum of domains, encompassing content recommendation systems, user behavior analysis, sentiment analysis, targeted advertising, and the enhancement of search algorithms for improved relevance and user engagement. Supported by illustrative case studies, this study elucidates how LDA serves as a potent mech