Workshop: Unsupervised Machine Learning and Text Analysis – University of Copenhagen

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12 December 2017

Workshop: Unsupervised Machine Learning and Text Analysis

On 12 December 2017, PhD Fellow Golovchenko is to facilitate a workshop on Unsupervised Machine Learning and Text Analysis at University of Waseda, Tokyo.

The workshop will present the core principals behind the unsupervised machine learning approach towards text with an emphasis on Structural Topic Modeling (STM). Furthermore, the event will offer practical hands-on experience with topic model analysis of text in R.

Social scientists have used quantitative content analysis for decades to understand the breadth of frames, topics and discourses. The approach can be used for descriptive purposes, such as the study of policy change throughout time. It can also be used for causal inquiries, such the study of the media’s effect on political opinions and attitudes among citizens.  Manual content analysis, however, can be both highly time consuming and limited to only a small proportion of the text corpus. The workshop will introduce the recent advancements in Topic Modeling as an attempt to automate content analysis in order enable a study of large data sets. Furthermore, the workshop will lead to a discussion of the benefits of topic modeling in relation to other methodologies within both computer science and social science.