I-could-have-written-that: Difference between revisions

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|Creator=Manetta Berends
|Creator=Manetta Berends
|Date=2016
|Date=2016
|Bio=Manetta Berends (1989, NL) has been educated as a graphic designer at the Artez art academy in Arnhem before starting her master-education at the Piet Zwart Institute (PZI), Rotterdam. From the Artez academy she acquired an interest in typography and research based design, which she expanded on in her research projects at the PZI, where she mainly focused on the systemization of language in the field of natural language processing and text mining.
|Bio=Manetta Berends (NL) has been educated as a graphic designer at ArtEZ, Arnhem before starting her master-education at the Piet Zwart Institute (PZI), Rotterdam. From an interest in linguistics, code and a research based design practice, she currently works on the systemization of language in the field of natural language processing and text mining.
|Thumbnail=I-could-have-written-that these-are-the-words mb.png
|Thumbnail=I-could-have-written-that these-are-the-words mb 300dpi.png
|Website=http://pzwart1.wdka.hro.nl/~manetta/i-could-have-written-that/
|Website=www.manettaberends.nl
|Description=Text mining algorithms are the hidden actors on the internet, that monitor social media platforms, are used in national security programs, and are starting to review essays written by students in school. Their results seem to be read directly from the data, but this research project shows that text mining results are not read nor mined, they are constructed. The lack of fixed standards, the effect of the metaphor of raw data, and the way in which the term mining disregards the human aspect in the creation of results, question if results are actually read from their source. With these three research topics, this project reveals the writerly nature of this technology.
|Description=''i-could-have-written-that'' is a two hour workshop, that critically examines the reading power of text mining software. The workshop dismantels how large sets of written documents are transformed into useful/meaningful/truthful information. A process that is presented by text mining companies as ''"the power to know"'', ''"the absolute truth"'', ''"with an accuracy that rivals and surpasses humans"''. This workshop challenges the image of this modern algorithmic oracle, by making our own. more info: [http://i-could-have-written-that.info www.i-could-have-written-that.info]
}}
}}


[[File:Perceiving-a-wordcloud.png|600px]]
<gallery>
File:I-could-have-written-that these-are-the-words mb 300dpi.png
File:Text-mining-is +1.00 certain.png
File:I-could-have-written-that categories-modality.gif
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''i-could-have-written-that'' is a two hour workshop, in which we will write between the lines, raw data will be cooked, and personal oracles will be trained. The workshop contains three parts. We start with a writing exercise in which we challenge the strictness of numbers with words. Then we will train our personal oracles by following the 1989 model Knowledge Discovery in Databases, and do manual pattern recognition with notecards and pens. After this hard work, we will ask three algorithms to do a bit of work as well. They will built your personal oracle, which you can consult at any time in the future!
 
Many text mining services offer pre-trained classifiers through API's without specifying how the system is created. They promise to read meaning out of a text correctly. This unconditional trust is the basis of automated essay scoring systems (AES), psychology studies, job vacancy platforms and terrorism prevention. Automated systems are trained to detect sentiment, education level, mental illness, or suspicious behaviour. The techniques are used online in i.e. social media platforms, in the academic field and by the government. However, framing text mining techniques as readerly systems makes them immune for questions and discussion. When text mining results are considered as a product of writing, the presence of an author is emphasized. This makes it possible to discuss text mining results in relation to predefined goals and breaks the idea that text mining results represent an absolute truth.

Latest revision as of 16:05, 13 February 2017

I-could-have-written-that
Creator Manetta Berends
Year 2016
Bio Manetta Berends (NL) has been educated as a graphic designer at ArtEZ, Arnhem before starting her master-education at the Piet Zwart Institute (PZI), Rotterdam. From an interest in linguistics, code and a research based design practice, she currently works on the systemization of language in the field of natural language processing and text mining.
Thumbnail
I-could-have-written-that these-are-the-words mb 300dpi.png
Website www.manettaberends.nl

i-could-have-written-that is a two hour workshop, that critically examines the reading power of text mining software. The workshop dismantels how large sets of written documents are transformed into useful/meaningful/truthful information. A process that is presented by text mining companies as "the power to know", "the absolute truth", "with an accuracy that rivals and surpasses humans". This workshop challenges the image of this modern algorithmic oracle, by making our own. more info: www.i-could-have-written-that.info



i-could-have-written-that is a two hour workshop, in which we will write between the lines, raw data will be cooked, and personal oracles will be trained. The workshop contains three parts. We start with a writing exercise in which we challenge the strictness of numbers with words. Then we will train our personal oracles by following the 1989 model Knowledge Discovery in Databases, and do manual pattern recognition with notecards and pens. After this hard work, we will ask three algorithms to do a bit of work as well. They will built your personal oracle, which you can consult at any time in the future!

Many text mining services offer pre-trained classifiers through API's without specifying how the system is created. They promise to read meaning out of a text correctly. This unconditional trust is the basis of automated essay scoring systems (AES), psychology studies, job vacancy platforms and terrorism prevention. Automated systems are trained to detect sentiment, education level, mental illness, or suspicious behaviour. The techniques are used online in i.e. social media platforms, in the academic field and by the government. However, framing text mining techniques as readerly systems makes them immune for questions and discussion. When text mining results are considered as a product of writing, the presence of an author is emphasized. This makes it possible to discuss text mining results in relation to predefined goals and breaks the idea that text mining results represent an absolute truth.