Have you considered using this for analyzing feedback for politicians? They have similar pain points in understanding what feedback from constituents is a general problem vs isolated concern. Maybe through twitter data (as PoC) and then actual emails from constituents.
That's a great idea. Once we have training data for a new category it does not take us a long time to create a ML model for it. Are you aware of any such corpus? ;-)
I am not aware of a corpus for political sentiment specifically. There's a general twitter sentiment dataset[0], the link appears to be broken but it's what everyone cites, not sure why it's down.
This paper[1] uses tweets and emoticons in the tweet as a soft label for sentiment, there's obvious issues with that, but it's a cheap way to get lots of noisy labels.
Ah -- I thought you were talking about classification of these tweets so that politicians know what their followers are talking about. Sentiment analysis is a very small part of what we do and as you said there are tons of examples on web that use Twitter's data in their model.
>I thought you were talking about classification of these tweets so that politicians know what their followers are talking about. Sentiment analysis is a very small part of what we do and as you said there are tons of examples on web that use Twitter's data in their model.
I was, I was guessing that you had an general topic/clause segmentation model + sentiment analysis, sounds like you're saying the topic/clause (or issue) segmentation model is pretty domain specific, so what new datasets you'd need to for political issues build is beyond me, but I think it'd be well worth it. Connecting politicians and constituents is a pretty universal need.
