Jeffery M. Binder’s article on text mining, language standardization, and their application to the humanities brings several questions into discussion. It is nothing short of fascinating how a computer can automatically identify specific topics based on criteria that a system searches for in any length of text. Since this idea of “topic modeling” has been applied to the humanities through programs such as MALLET, the attempt and effort to determine topics based on clusters of information has increased greatly since the late twentieth century. From some of the first computer systems developed at universities to process short boxes of text to output predictions in fields such as politics, to the latest programs embedded into our cellphones that take essentially every word of text we input into account, it seems that the biggest challenge facing the rapidly expanding use of topic modeling has stayed consistent. There is an inevitable bias toward standardized forms of language use. This issue is also apparent in other text-mining methods that depend on statistical analysis of words, because it’s such a difficult one to overcome in a computer. For example, there are text analysis software programs that are designed to guess the emotional state of a group of words, but the problem is that no matter how elaborate a program may be coded a computer will never understand the emotional values and ever-changing expressions of human beings. Binder’s mentions metaphors, irony, and dark humor in text as some of the unbeatable emotional obstacles from humans that text-mining programs must overcome, but often struggle with greatly.<\/p>\n","protected":false},"excerpt":{"rendered":"

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