Thank you for your questions, Jan.

Is this on questions on Wikipedia Articles which ask for an estimate of good, neutral or bad assertions (or generally sentiments) about a subject?

After the Signpost ran a blurb last month on research successfully predicting company stock price changes using pageviews (confirming similar work from 2013), I tried to find anyone using the textual substance of edits to do the same thing. I found this:

http://community.wolfram.com/groups/-/m/t/882612

It produces small but consistently positive correlations between companies' article edit summaries classified by the text sentiment model which ships with Wolfram Mathematica and their daily stock price changes. The significance is low, in part because using sentiment of edit summaries is a very naive approach. So I wonder if anyone has tried to train a sentiment analysis model to address the task directly with full diffs.

Or are you more interested in the subject of lobbyism and company directed edits and the like?

I'm more interested in identifying organized advocacy, and I suspect such models would help with that, too, especially if brand product articles are included along with companies.

2016-12-01 4:12 GMT+01:00 James Salsman jsalsman@gmail.com:

Who, if anyone, is examining crowdsource survey questions such as, "Look at the text added or removed in this edit to [Company]'s Wikipedia article. Was the editor saying [ ] good things, [ ] bad things, or [ ] was neutral about [Company]'s financial prospects?"?