I’m presenting the paper below at SIGDOC ’16 in Silver Spring, Maryland today. The paper is the outgrowth of an RSA workshop that Bill Hart-Davidson and Ryan Omizo led in Madison, WI, in 2015. Here’s the abstract:
This paper reports on the results of an intensive application development workshop held in the summer of 2015 during which a group of thirteen researchers came together to explore the use of machine-learning algorithms in technical communication. To do this we analyzed Amazon.com consumer electronic product customer reviews to reevaluate a central concept in North American Genre Theory: stable genre structures arise from recurring social actions. We discovered evidence of genre hybridity in the signals of instructional genres embedded into customer reviews. Our paper discusses the creation of a prototype web application, “Use What You Choose” (UWYC), which sorts the natural language text of Amazon reviews into two categories: instructionally-weighed reviews (e.g., reviews that contain operational information about products) and non-instructionally-weighed reviews (those that evaluate the quality of the product). Our results contribute to rhetorical genre theory and offer ideas on applying genre theory to inform application design for users of information services.
Here is the full paper:
And here are my slides from today, along with my speaker notes: