Will Artificial Intelligence Replace the Journalist?

This paper was presented at the 2019 Future Communications conference at York University. Download it as PDF here, with all of the proper in-text citations. 

An exhausted editor waits in line for her second coffee of her overnight shift, when a notification on her smartphone causes her to abandon her quest for caffeine and bolt back to the office. Glancing back at her phone she can hardly believe the headline, “9.0 magnitude earthquake hits off the coast of Vancouver, tsunami warning issued.” It has only been 30 seconds since early warning systems off the coast of British Columbia detected the quake, and before the editor has even made it back to the newsroom, she’s already approved story copy for a first draft of what will become Canada’s biggest story for a generation. The out of breath editor sprints to her desk and watches social media in real time as B.C. residents react to the devastating effects of the tremors. While other news organizations confirm the details of the quake and struggle to piece together a readable breaking story, the editor who is working alongside a small skeleton crew of overnight reporters, writers and techs, is already assigning reporters and chasing the story on the ground.

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Which is the most negative political Party? What does sentiment analysis tell us?

When I set out to do sentiment and toxicity analysis of Canada’s Question Period sessions, I held out hope that all the number crunching would reveal some hidden insight into Canadian politics. Perhaps something lurking a bit below the surface that only a bit of Python, a few APIs and TextBlob could reveal.

Sadly that hasn’t happened but the whole exercise has revealed a few things that surprised me.
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Sentiment analysis on Question Period speeches using TextBlob, the Perspective API and Python

This past winter I was teaching a class on Social Networks at George Brown College and we examined how many companies are using social media sentiment analysis to get further insight on the public’s perception of their brand and products.

We went through a few great examples of how computer generated sentiment analysis was being used in a variety of fields including journalism. Vox’s article from 2016, where they did sentiment analysis on seven months of Donald Trump’s tweets, inspired me to move beyond abstract discussions about sentiment analysis and figure it out for myself.
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