Ars Technica: Snark Attack — Cornell Students Teach Software to Detect Sarcasm

Last fall, a group of Cornell Tech students collaborated on a Company Challenge with Bloomberg to productize sarcasm. What resulted was a machine learning app called TrueRatr, reports Ars Technica.

Christopher Hong of Bloomberg acted as mentor to the interdisciplinary student team behind TrueRatr (consisting of MBA candidates, engineering, and design graduate students)—Mengjue Wang, Ming Chen, Hesed Kim, Brendan Ritter, Shreyas Kulkarni, and Karan Bir. Hong had researched sarcasm detection himself while working on his 2014 master’s thesis. “Everyone uses sarcasm at some point,” Hong told Ars. “Most of the time, there’s some intent of harm, but sometimes it’s the opposite. It’s kind of part of our nature.”

So it should be really easy for software to detect sarcasm… not. The problem has been that “the definition of sarcasm is not so specific,” Hong explained. Past efforts to catch sarcasm have used techniques like watching for cue words (“yeah, right”), or the use of punctuation, such as ellipses. But in his research, Hong looked at what he calls “sentiment shift”—the use of both positive and negative words in the same phrase.

Read the full article on Ars Technica.