Topic Modeling and Semantic Analysis are advanced techniques in natural language processing that aim to uncover hidden patterns and relationships within textual data. Topic modeling methods, such as Latent Dirichlet Allocation (LDA) and Non-Negative Matrix Factorization (NMF), enable the extraction of underlying themes or topics present in a large collection of documents. These topics provide insights into the main concepts and discussions within the data.
Text data is all around us, from social media posts to research articles, and news headlines to product reviews. Understanding the hidden structures and meanings within text can be a daunting task, but it’s an essential one in today’s data-driven world. In this article, we’ll delve into the fascinating realms of Topic Modeling and …