计算机科学
过程(计算)
人工智能
文字嵌入
主题模型
词(群论)
情绪分析
意义(存在)
自然语言处理
数据科学
机器学习
嵌入
语言学
认识论
操作系统
哲学
作者
Marta Villamor Martin,David A. Kirsch,Fabian Prieto-Nañez
标识
DOI:10.1080/17449359.2023.2181184
摘要
Building upon our experience implementing a mixed method study combining historical and topic modeling techniques to explore how institutional voids are resolved and their relationship to formal/informal markets, we describe the promise of Topic Modeling techniques for historical studies. Recent advancements – particularly improvements in artificial intelligence and machine learning techniques – have enabled the use of off-the-shelf AI to analyze and process large quantities of data. These techniques reduce research biases and some of the costs previously associated with computational text analysis techniques (i.e. corpus processing time and computational power). We highlight the usefulness of three text analysis techniques – structural topic modeling (STM), dynamic topic modeling (DTM), and word embeddings – and demonstrate their ability to support the generation of novel interpretations. Finally, we emphasize the continuing importance of the author in every step of the research process, especially for abstracting from AI outputs, evaluating competing explanations, inferring meaning, and building theory.
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