Time and lineage in text analysis
![Image](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgDZuF9e0lJQYNRWZeW5aPO_ySZPEM-_FeU9vCC3MeR9WuIzcmG8q13_AsWQxsFAezhJXljAbyH8-H553M1VZRinJsFQiWvD_skvpdKoMoT61y2wcgdJEVCKP0VD5fsVs5nwI4u8axUFUe9/s16000/timechunks.jpg)
I've been working on a few parallel projects that involve topic modeling abstracts of managment research publications (See Topics in Management Research , for example). My method of choice at the moment is Latent Dirichlet Allocation . I'm finding that this class of methods is useful in ways I hadn't quite expected when I started this exercise about a year ago in order to be able to test some hypotheses about management research in a causal inference/regression context. Since then I have been exploring the use of topic modeling for qualitative analysis. This sort of use of topic modeling is well represented in modern management research (See Hannigan et al., 2019 , a tremendous, encyclopedic review of the subject). Sidebar: I'm still grappling with the distinction between quantitative and qualitative research. The terminology is deployed, at least in the circles I'm in, in a way that implies an eqivalence between qualitative and inductive research, and quantitativ