‘The NaNoGenMo works require varying degrees of human input.’.‘I’m actually excited about novels that are co-authored between humans and code.’.‘He also points out that the writers of the algorithms bring their own human experiences to bear on their coding, adding a necessarily human element.’.‘Ray Kurzweil has predicted that by 2029, computers will be able to outsmart even the cleverest human.’.‘The result is simple but effective, and on a quick read, perfectly human.’.‘McCann Japan decided to pit its AI director against the human creative director to create a 30-second ad for Clorets gum.’.I’ve sorted them into rough categories, because I care about you. For example, some uses of the word ‘human’ from the cited articles are included below. It’s all well and good that the word ‘human’ pops up, but that doesn’t necessarily tell us very much. The problem with word clouds is that the comprising words are removed from their semantic contexts. And then I got some words that relate to the program/content developers and consumers themselves, like ‘people’, ‘human’, ‘companies’, and ‘business’. I also got words that are frequently associated with anything related to computers, such as ‘computer’, ‘technology’, ‘algorithm’, ‘program’. Instead, I got words that are obviously related to National Novel Generation Month, like ‘Kazemi’ (NaNoGenMo is Internet artist Darius Kazemi‘s brainchild, ‘NaNoGenMo’, and ‘novel’. Quite honestly, I was hoping for/anticipating words like ‘fear’, ‘excitement’, and ‘fantasy’ to pop up so that I’d have something interesting to use as a conversation starter at my next cocktail party. We often regard news agencies as simply reporting on world events, but writers’ biases are inevitable: writers choose what words to use for rhetorical effect, and they choose what content to include and omit to best make their points. My hope when generating a word cloud of news article text was that words revealing underlying social attitudes towards natural language generation and computer-generated texts would rise to the surface.
REDDIT WORD CLOUD GENERATOR GENERATOR
The word cloud generator pulled from a corpus of 10,701 words, but from these words it ignored stop words like ‘the’ and ‘and’. This word cloud was generated using the body text (titles, bylines, and long-form quotations omitted), of the news articles listed below, which I found by seaching ‘NaNoGenMo ’ in Google News on July 14, 2017. It is not the result of much academic deliberation, but is rather the result of me testing all of the available tools (even the tacky ones) for quantitative literary analysis. Rest assured, dear reader, that this word cloud is no way going to pop up in my final thesis. They look a bit tacky, many of the articles they’re thrown into aren’t much enhanced by their presence, and they always make me think of a beginner’s quilting club where none of the participants actually knows what quilting is.Īnd then I made my own word cloud.