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Something I found very interesting in Jeremy Binder’s article about text mining is that computers seem have great difficulty when dealing with the human reality of language. Binder argues that when studying literary and cultural texts, text-mining softwares can pull out key words or sentences, but in analysis it focuses primarily on the literal meanings of words. This could lead to many misinterpretations, as language and slang change so frequently over time that evaluating texts on literal meaning could lead to false conclusions about the information presented. As a result, as Binder recognizes, text-mining is often better utilized as “statistical methods in applications like search engines, spellcheckers, autocomplete features, and computer vision systems”. This makes sense, because these application don’t take into the account the fluid nature of language and they search strictly based off spelling or literal meaning.

 

 

In response to “Money=Happiness?”, by ZC, I agree! This studying presents itself with a convincing case, but when you really break down the method of data collection, you can start to realize that it doesn’t sound too convincing. You also referenced a point which I overlooked, the sample size is way too small, I agree, good point.