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Over the last two decades, social research has witnessed an unprecedented surge in accessible textual data, propelled by the remarkable advancements in computational methods. From extensive records of century-old legislative speeches to the colossal volume of social media content reaching hundreds of millions, textual data has become intrinsic to contemporary social science research. Understanding and effectively harnessing this data has never been more crucial.
This concise yet comprehensive course is designed to equip you with the statistical expertise needed to navigate and extract insights from textual data of varying sizes. From collection and preprocessing to detailed analysis, you will gain familiarity with prevalent quantitative text analysis techniques, learning to discern and implement suitable methods for their research challenges.
The course begins with foundational aspects, including document processing and description, progressing to advanced methodologies like sentiment analysis, scaling techniques, supervised machine learning, and topic models. At the end, you will be familiar with contemporary quantitative text analysis methodologies, gaining expertise to select and apply suitable techniques to address your specific data and research problems effectively.
