“It is manifestly impossible to read everything, and it has always been so. The utility of the digital corpus— despite its vaunted claims of “increased access”— only serves to make the impossibility of comprehensive reading more apparent.”
Of all the articles we have read, I believe this is the more relevant, insightful, and true statement this semester, by any author. Ramsey is spot on in his defense of the digital humanities, because in one statement here, he has both admitted its flaws and exemplified its purpose. The goal of Digital Humanities is not to gain comprehensive readings on the texts being analyzed, but instead to provide data that humans could never do. As he puts it, “it is unlikely that a human being, even if asked to name only the top three words in each text, would produce these lists precisely as the machine gives them to us”. This is the true power of digital analysis, it provides information otherwise unobtainable.
Similarly, regarding comprehensive readings, humans possess the ability to evaluate semantic meanings of texts and understand context on a level a computer could never achieve. Ramsey, discussing students biases on books, says “Books come to them as high or low, deep or shallow, hard or easy, read “for pleasure” or read “for class;’ with dozens of gradations in between”
This kind of emotional or semantic analysis is deeply and inherently human, and something that a computer would not be able to comprehend in full. Yes a computer can understand that certain words are inherently shaded one way or another, but it is prone to mistakes. Awesome, for example, could be used to describe Hiroshima or your cousins wedding last weekend. Someone’s heart can skip a beat when they are about to crash a car, or when they are leaning in for a kiss. Words, phrases, context, language, are all inherently human and require complex thought in order to understand.
For these reasons, I just believe it is currently beyond the capacity of computers to analyze semantics and emotions, given the evidence presented by Topic Modeling.
