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An article expresses how topic modeling and text mining can make discoveries of the past. In this article using textual data and the text mining of multiple journals, essays, and letters, the researchers found the fascinating story of James Hemings.  Jefferson’s servant cook’s story is a story that would’ve been found without the help of text-mining. The lack of using his name, his outstanding skills and significance to Jefferson in literature made it impossible for historians to recognize the importance of Hemings. But thanks to text-mining, these researchers found patterns and consistencies of hundreds of letters and journals which lead them to the story of Jefferson’s cook. The article states: “Its exhilarating to think of the many ways in which digital tools might transform the archive of American slavery— pushing forward theories about the archive, arguments about its contents, and new forms of criticism that illuminate the past and inform the present.” Especially with suppressed groups throughout history, text-mining may be a way for us to dig out the ignored past of people.  A study in the “Digital Humanities”, too, focused on how social networks in texts could help us find relationships within countries — specifically Britain. But just like the Hemings article, biases in literature contribute to the meddling of data. The undermining of an individual based on their race, gender and religion, present in the 17th and 18th century, would heavily impact the results of the study. In recognizing this, the study stated: “A bias towards men is a known issue in existing historiography; this bias is neither confined to the ODNB nor particularly surprising. However, transforming textual secondary sources into visual representations allows for more purposeful “critical scrutiny of what is known, how, and by whom”. In response to last weeks readings, RF stated that “in some cases, the debate surrounding topic models is too concerned with the success of the algorithm itself opposed to the human space that the algorithm is working in”. In this, he means that the field of algorithms and the humanities are still rather polarized. As researchers in the humanities become more accustomed to the use of data analysis and text-mining,  society becomes a more connected and organized system.