Textmining allows you to use computer software to conduct an extensive quanitative analysis of text. Textmining is most useful in conducting an analysis of large corpora in a database or archive. Specifically, textmining can be used to search through text to quanitify how often a certain word appears in a digitized archive or material. Textmining tools can be used to examine patterns or trends in qualitiative data. Several projects that have utilized textmining include America’s Public Bible, Robots Reading Vogue and Signs40.  In the case of America’s Public Bible, textmining was used to perform an analysis of biblical quotations in US newspapers to determine trends in biblical references at different historical periods. Robots Reading Vogue was designed to evaluate beauty trends over time while Signs40 used textmining to determine changes in Signs’ feminist scholarship. Trends related to certain words, topics and themes can then enable a researcher to draw certain conclusions on the historical time period of study.

Free text mining tools like Voyant can be used to conduct textual analysis. For instance, you can upload links of textual information to Voyant as a dataset that will then be processed into a digital corpus. From there,  tools like Cirrus, Reader, Trends, Summary and Contexts can be used as visual interactives to find patterns or differences in the data. The dataset used for my exploration of Voyant was the WPA Slave Narratives.

I utilized all of Voyant’s given tools to compare the frequency of words in various states. Specifically, I compared the states of Alabama and Maryland to each other by seeing the distinctive words utilized in their interviews.  For instance, Alabama used distinct words like  didn’t (357), alabama (138), ain’t (213), don’t 182), ca’se (69), caze(104), couldn’t (103), i’s (93), i’se (92), dat’s (90). Maryland used distinct words like  bowie site. (17), baltimore (64), ellicott(11), cockeysville (11), arundel (9), annapolis (9), md (11), maryland (60), rezin(7), manor (6). From this analysis, I was able to conclude that Alabama’s distinct words referred to their unique dialect while Maryland’s distinct words referred to Maryland African American communities. In using Voyant, I was able to see that while textmining is useful in searching through large sets of data-it is imperative that a person is working in conjuction with the software to get rid of unnesessary “stop” words and to make sense of the words that are repeated throughout to draw historical conclusions.

Metadata is “structured textual information that describes something about the creation, content or context of a digital resource” (Jisc, 2014). Metadata enables items to be discoverable in a database or archive due to the field information provided. Researchers, patrons and digital humanists can use the metadata provided in common fields like title, subject, description, creator, date, format, type and location to find artifacts and documents.The common fields present digital humanists with a source’s context and descriptive infromation. Context and description are the most essential parts of metadata as they help digital humanists understand “the physical and digital origin, structure, and technical makeup” of a resource (Carbajal and Caswell, 2021, pg.1108 ). From this, digital humanists can improve their own understanding of their research and appropriately utilize primary and secondary sources in the digital humanities.

Additionally, metadata serves a purpose in maintaining an archive’s material infrastructure which can then aid the digital humanities as a whole. Maintaining infrastructure through the preservation of physical and digital records will feed into the ever changing architectural, technological, social, epistemolgical, and ethical structures of an archive (Carbajal & Caswell, 2021). Consequently, digital archivists can fullfill the needs of their digital users by adopting improved metadata standards to extinguish problematic metadata. The continued maintenance of material infrastructure has the power to place checks on the political, historical, and cultural biases of the archivist or archive in order to better satisfy the needs of a diverse population of digital users.

Tropy and Omeka can assist digital humanities practitioners in research organization and collaboration. Trophy is a software tool and unified system that enables researchers to manage and describe the image files of archival materials. Archival photographs can be saved and viewed for future reference within Tropy.  Specifically, common field information for resources can be saved within the software and organized into various projects and collections. Similarly, Omeka serves as a computer software that assists researchers in organizing their materials. Omeka also has interoperability which allows users to share Tropy’s archival metadata with a larger audience. For instance, I am now able to share my findings related to Service Guardian and Hamilton Beach cookware. Software like Tropy and Omeka enabled me to become more thorough with locating and writing the correct metadata. Since Omeka is a resource that can be shared with others, I wanted to make sure that I was using the correct subject, name, title, and name authorities for my kitchen collection. I also wanted to develop enough accurate background knowledge on my kitchen items to provide the correct contextual and descriptive information. Fortunately, Tropy and Omeka enable users to follow the best practices in maintaining metadata by offering archival organization and information sharing.