This master's thesis, conducted in the company Claravista, a consulting firm based in Paris specializing in digital marketing and data science, aims to comprehensively compare various topic modeling algorithms. The primary focus of this research is on natural language processing, specifically delving into topic modeling techniques. In an era where a staggering volume of information is generated daily by individuals across social media platforms, the potential inherent in analyzing this wealth of data resonates profoundly across numerous domains. Especially in the fast-paced world of digital marketing, where Claravista works, this kind of analysis becomes even more important. Throughout this study, three distinct machine learning algorithms—namely, Latent Dirichlet Allocation (LDA), Top2Vec, and BERTopic—are evaluated in the context of topic modeling.