{"id":620,"date":"2021-02-22T03:47:53","date_gmt":"2021-02-21T19:47:53","guid":{"rendered":"https:\/\/fass.nus.edu.sg\/methods-school\/?page_id=620"},"modified":"2025-02-25T09:59:42","modified_gmt":"2025-02-25T01:59:42","slug":"quantitative-text-analysis-2","status":"publish","type":"page","link":"https:\/\/fass.nus.edu.sg\/methods-school\/courses\/quantitative-text-analysis-2\/","title":{"rendered":"Quantitative Text Analysis 2"},"content":{"rendered":"\r\n

Quantitative Text Analysis 2<\/h1>\r\n

Prof. Madrid-Morales is an outstanding instructor, extremely passionate and helpful. I hope he will continue to offer this great course.<\/em> – participant from Hong Kong<\/p>\r\n

This course offers students an advanced exploration of computational text analysis methods to systematically extract insights from text data. It builds on the foundational concepts covered in Quantitative Text Analysis 1<\/a>\u00a0and introduces students to more sophisticated techniques, including unsupervised machine learning, word embeddings, transformer-based models and Large Language Models. Students will learn how to implement these methods using R and apply them to diverse research questions. Students will also gain experience with multilingual datasets and networked representations of text, addressing complex challenges often encountered in advanced text analysis.<\/p>\r\n

This course is the second part in a two-course sequence. It requires participants to be familiar with the material covered by the introductory Quantitative Text Analysis 1<\/a>\u00a0or have prior experience with basic computational text analysis, preferably using the R programming language.<\/p>\r\n

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Dates<\/h2>\r\n

This one week, the 17.5-hour course runs Monday-Friday, 7 – 11 July. The course is scheduled for 9:00 am – 12:30 pm.<\/p>\r\n

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Classroom Location\u00a0<\/h2>\r\n

Faculty of Arts and Social Science<\/strong><\/p>\r\n

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Instructor<\/h2>\r\n

Dani Madrid-Morales<\/a>, University of Sheffield<\/p>\r\n

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Detailed Description<\/h2>\r\n

This course provides an applied and advanced introduction to quantitative text analysis, focusing on methods that allow participants to uncover latent structures in text data, work with semantic representations, and analyse multilingual or networked corpora. Designed for students with some experience in R and basic computational text analysis, the course helps learners extend their skillset with techniques for handling larger and more complex datasets.<\/p>\r\n

The main learning objectives of the course are:<\/p>\r\n