A Multidimensional Framework for Evaluating Lexical Semantic Change with Social Science Applications
This work addresses the need for a unified computational methodology for historical linguists and social scientists to analyze lexical semantic change, though it appears incremental as it integrates existing forms into a new framework.
The authors tackled the problem of evaluating lexical semantic change by presenting a three-dimensional framework integrating sentiment, breadth, and intensity, along with frequency and collocate shifts, enabling systematic mapping with applications in computational social science. They demonstrated this with an analysis of semantic shifts in mental health terms, revealing patterns related to pathologization and stigma.
Historical linguists have identified multiple forms of lexical semantic change. We present a three-dimensional framework for integrating these forms and a unified computational methodology for evaluating them concurrently. The dimensions represent increases or decreases in semantic 1) sentiment, 2) breadth, and 3) intensity. These dimensions can be complemented by the evaluation of shifts in the frequency of the target words and the thematic content of its collocates. This framework enables lexical semantic change to be mapped economically and systematically and has applications in computational social science. We present an illustrative analysis of semantic shifts in mental health and mental illness in two corpora, demonstrating patterns of semantic change that illuminate contemporary concerns about pathologization, stigma, and concept creep.