School of Computer Science
Technical Workshop Series
Workshop Title: Python Programming for Topic Modeling: NLP Techniques for Data Analysis and Insights
Presenter: Soroush Ziaeinejad
Location: 4th Floor (Workshop space) at 300 Ouellette Avenue (School of Computer Science Advanced Computing Hub)
In this hands-on workshop, you will gain practical experience in Python programming for NLP tasks, with a focus on topic modeling. In addition to topic modeling, you will also learn other advanced NLP techniques for data analysis and insights, including sentiment analysis, named entity recognition, and text classification. Through a series of guided coding exercises, you will gain experience in implementing these techniques using popular Python libraries such as NLTK, gensim, and scikit-learn. By the end of the workshop, you will have a deeper understanding of how to leverage topic modeling to extract meaningful insights from unstructured text data, and the hands-on experience to apply these techniques to your own projects.
·What is topic modeling, and how can it be used to analyze unstructured text data?
·How can text data be preprocessed for topic modeling?
·How are topic models built using popular Python libraries?
·How is the performance of a topic model evaluated?
·How do you select the appropriate NLP technique for specific data analysis needs?
·How can the skills learned in this workshop be applied to your own projects and research?
Basic knowledge of Python and mathematics
Soroush is a Ph.D. student and research assistant at the School of Computer Science. His main research area is Natural Language Processing and Information Retrieval on social networks.