In a guest post for OJB, Barbara Maseda looks at how the media has used text-as-data to cover State of the Union addresses over the last decade.
State of the Union (SOTU) addresses are amply covered by the media —from traditional news reports and full transcripts, to summaries and highlights. But like other events involving speeches, SOTU addresses are also analyzable using natural language processing (NLP) techniques to identify and extract newsworthy patterns.
Every year, a new speech is added to this small collection of texts, which some newsrooms process to add a fresh angle to the avalanche of coverage.
This week I’m rounding off the first semester of classes on the new MA in Data Journalism with a session on artificial intelligence (AI) and machine learning. Machine learning is a subset of AI — and an area which holds enormous potential for journalism, both as a tool and as a subject for journalistic scrutiny.
So I thought I would share part of the class here, showing some examples of how the 3 types of machine learning — supervised, unsupervised, and reinforcement — have already been used for journalistic purposes, and using those to explain what those are along the way. Continue reading →