I’ve had this idea for doing clustering and data mining of history texts. Would be interesting to creating learning algorithms that can learn timelines and context. Essentially history textbooks are structured possibly geographically and as a time series of events. Then doing graphical analysis and social analysis on these structures. Could compare history texts and see what is left out and maybe what each text places more emphasis on what events. Also, doing this across time to see how history texts have changed in what the historians themselves find interesting. Possibly finding patterns in history itself that were not evident or obvious without such algorithms that can crunch large large volumes quickly. Doing the normal sentiment analysis as well.
This could then lead to producing a better picture of different countries and people groups and how they were formed. Possibly doing anomaly analysis or creating other types of filters to uncover gaps in the history texts themselves.
This of course seems like it should have been done. Main issue is getting digital copies of the history books for the algorithm to work with. So, it may not have been studied. Creating learning algorithms that can understand human history seems like an important area of research. Especially as we are writing history now it is important to maintain a grasp of the entire picture and how everything fits together.