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Topic modeling is a statistical technique that allows the discovery of hidden thematic structures or “topics” in text. Topic modeling algorithms allow us to summarize, search, and explore large collections of documents in new ways. This talk will introduce Topic Modeling and go through the different types of algorithms and implementations available. The talk will give an overview of gensim, a Python library for Topic Modeling, and describe the process of data cleaning, model training, and results interpretation.
Mohamed Amin is a Masters candidate in the Technology Innovation Management program at Carleton University with a Software Engineering background. His current research focuses on business models and differentiation in the API Economy. He worked in the Telecom industry before joining Carleton University and did consulting work for local startups in Ottawa.