[oelmekki]

Great job, congratulations!

My first reaction was : "well, if it's sentiment analysis, it doesn't know anything about if the news is bad or good, only about the mood of writer". But I then realized this is actually even better. I don't want to filter out news that are not good news, this would be plain denial. For a same news, an article can be written in a positive and analytical way, or trying to incite hate or bad feelings. This is the later I want to filter, and sentiment analysis is probably the perfect tool for that.

I would love to know how you built your training dataset (how good and bad labels were decided), because that's ultimately the choice that shapes the whole decision process. Maybe this should be a standard kind of page for products offering ML based filtering.

Also, thanks a lot for providing an "all stories" tab, additionally to "good stories" and "bad stories", this is something automatic curated content misses too often. I really love the "stories to read" mode of google now, which provides me stories based on my interests, that's basically the first thing I check every morning. But I always wonder, when I read news from there : "is this a thing for the whole world or just for me?". We need referential, the possibility to see the whole picture and to switch easily between "content for me" and "content for the world" to take advantage of the bubble without being harmed by it.