I’ve recently become interested in Word2Vec as a way to represent semantic relationships between words in a corpus. In particular, I’m interested in making comparisons between corpuses: how do different texts organize concepts differently? Here I attempt to sketch a theoretical basis for word2vec drawing from early structural linguistics and sociology. Then I examine some basic results from training a word2vec model on the Gutenberg texts built into the nltk python library. Might this approach have utility for understanding how authors organize different concepts in a text?
Last year I came across a working paper for AJS on Belief Network Analysis by Andrei Boutyline . The paper looks at American National Election Survey data and examines two theories for the process of political opinion formation: Lakoff’s Theory of Moral Politics and Campbell’s Theory of Political Identity. This project, in collaboration with Sujaya Maiyya, was focused on extending BNA to the American National Election Survey timeseries data to test some of the claims Andrei made in response to an investigation from Delia Baldassarri using Relational Class Analysis . The original work was performed by analyzing survey data from the year 2000, but we argue that no claims can be made about this process unless we make a longitudinal investigation.
Several of my projects over the last few months have leaned in the direction of longitudinal studies. The question every sociologist asks is “how did we get here?”, so it makes sense that one would like to explore how things have been changing before now. My conclusion is that if networks provide meaningful investigation into the types of questions we are trying to answer, then we need to understand how these networks change over time.
This Python library is useful because it allows one to easily transition from a traditional networkx object to simple time series dataframe representations in Pandas and Numpy. I’ve already used it in several projects and I hope you can use it too!