This code accompanies my senior thesis project, Temporal Exploration of the Proceedings of Old Bailey. You can read the full document here.

The following descriptions relate to code in the old-bailey directory. Follow the instructions in this document to obtain the necessary files. To get more information about the code, see README.md files in each sub-directory. Run each file with the --help flag to get more info for each program.

To obtain the data used in this project, you will need two different datasets. For consistency with the instructions included in the README.md files, directory names are specified for each dataset:

1. The Proceedings of Old Bailey: Download the data from here and store the XML files in a directory called sessionsPapers.
2. The Ordinarys Accounts: The link in for downloading the Old Bailey data will also download the Ordinarys Accounts. Make sure and store the XML files in a directory with the name ordinarysAccounts.
3. London Lives (optional): The London Lives dataset is supplementary, but you can download it here and save it into a directory called londonLives. The structure is different than that of the previous two datasets, simply include all the subdirectories as is.

Once the data is obtained, you can combine the first two datasets into a directory called sessionsAndOrdinarys so all the Old Bailey data is together and can be processed at one time. Use this command:

mkdir sessionsAndOrdinarys
cp sessionsPapers/* sessionsAndOrdinarys
cp ordinarysAccounts/* sessionsAndOrdinarys

Now that you have the raw data, see data/README.md to learn how to process the data.

This code is associated with my senior thesis project.

The Proceedings of the Old Bailey, 1674–1913 (Hitchcock et al., 2012b) is a published record of criminal proceedings at London’s central criminal court. The Proceedings primarily depict the lives of the "non-elite" population of London. This project explores these proceedings to study this specific population over the approximately 250-year time period of the publication. Because the corpus spans a significant period of history, it can be examined to identify evolving patterns related to different social groups represented in the text. This project aims to identify which computational methods can reveal interesting sociolinguistic information about this corpus. More specifically, this paper will explore unsupervised techniques like latent Dirichlet allocation (LDA) (Blei et al., 2003), Word2Vec (Mikolov et al., 2013), and Embedded Topic Modeling (ETM) (Dieng et al., 2019b) when applied to the Proceedings of Old Bailey. Additionally, temporal variants of these methods, such as Dynamic Topic Modeling (DTM) (Blei and Lafferty, 2006), Dynamic Embedded Topic Modeling (DETM) (Dieng et al., 2019a), and LDA and Word2Vec manually run across different time slices, are applied to analyze the corpus over time.