This paper presents an intertemporal bimodal network to analyze the evolution
of the semantic content of a scientific field within the framework of topic
modeling, namely using the Latent Dirichlet Allocation (LDA). The main
contribution is the conceptualization of the topic dynamics and its
formalization and codification into an algorithm. To benchmark the
effectiveness of this approach, we propose three indexes which track the
transformation of topics over time, their rate of birth and death, and the
novelty of their content. Applying the LDA, we test the algorithm both on a
controlled experiment and on a corpus of several thousands of scientific papers
over a period of more than 100 years which account for the history of the
economic thought