Enhanced Topic Modeling for Data-Driven News Extraction Using Frequency Word Count Techniques

In response to the abundance of available information, this study aims to enhance data-driven news extraction by focusing on articles from CNN and Daily Mail. The purpose is to develop an approach utilizing Latent Dirichlet Allocations (LDA) model as well as Term Frequency-Inverse Documents Frequency (TF-IDF) techniques, specifically employing frequency word count methods. The methodology involves constructing extensive corpora and analyzing word frequency distributions to extract salient information and identify crucial themes and trends across multiple articles. TF-IDF scores are utilized to rank relevant terms within each topic, facilitating the generation of concise summaries. The study's findings demonstrate the effectiveness of the approach in identifying significant topics such as politics, economy, sports, health, technology, environment, entertainment, science, global affairs, and social issues, thereby enhancing the extraction of key insights from news datasets.