Topic Modeling
Topic modeling is a machine-learning method. “Machine learning” means that the program is designed to identify patterns (rather than defining them from the outset). In topic modeling, the patterns concern the occurrence of words across a large corpus, and they are called “latent” because their appearance depends on the application of the computer algorithm. The patterns relate to the likelihood or probability of words occurring together in the same document, with this co-occurrence of words is referred to as a “topic”. One good thing about topic modeling is that it can show the same word as having different meanings depending on the other words that (are likely to) co-occur in the same document. For example, all of the below topics feature the word “blood” quite prominently, but it has different meanings depending on whether it is alongside words about king, church and people (lineage), men, battle, and killed (bloodshed), or strange night-time horrors (bloodcurdling).
Australian Short Stories
This topic model uses the same set of short stories that underpin the Literary Mapping exercise. These are 28 stories by Australian authors published in Australian newspapers around the turn of the 20th century. The dynamic topic model offers students the capacity to adjust multiple parameters of the topic model so as to understand how this computational method for interpreting texts works. Students will also be able to move between overall themes and individual stories, which are short enough to read in the classroom.
Australian Literary Journals
This topic model includes the major journals of Australian literature: Southerly, Sydney Review of Books, Westerly, Australian Literary Studies, Antipodes, and the Journal of the Association for the Study of Australian LIterature. We anticipate that this topic model will be most relevant for teachers and upper secondary students. By exploring the model they can gain an overview of the major “topics” in Australian literary criticism to facilitate future research as well as experience with the way that topic models express and transform textual cultures.