Automated text mining, latent class analysis, and multi-dimensional scaling

IDEAS & Data Application Lab co-host this live webinar.

国际数据科学与工程协会 IDEAS

IDEAS is a global nonprofit organization that is dedicated to fostering the data engineering and data science ecosystems and broadening the adoption of their underlying technologies to accelerate the innovations data can bring to society. Our goal is to create a community to connect AI and Data Science enthusiasts. All of the conferences that IDEAS host will demonstrate cutting-edge technology and feature a variety of AI and Data Science experts covering topics including industry trends, real-world applications, open-source software, solutions-based case studies, and many others.

Topics: Automated text mining, latent class analysis, and multi-dimensional scaling: Which is the best?

Description: 

Today it is a common practice for data analysts to extract recurring terms and phrases from unstructured, textual data. Some text mining software packages automatically collapse the recurring terms and phrases into common threads (categories, concepts), but in this fully automated process the analyst must hand over his/her own judgment to the computer algorithm. Nonetheless, some other text mining software modules allow the analyst to conduct further analysis, such as latent class analysis and multi-dimensional scaling, for detecting the clustering pattern. In this seminar both methods will be demonstrated.

Speaker’s Profile:

Chong Ho Yu is a Professor of Behavior and Applied Science, Adjunct Faculty of Mathematics, and the Quantitative Research Consultant of the Office of Sponsored Research and Grants at Azusa Pacific University (APU). He is also the Committee Chair of Data Science Consortium and Big Data Discovery Summit at APU.

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