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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

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? ;-)

alexbeloi 8y ago
>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. [0]: http://www.sananalytics.com/lab/twitter-sentiment/ http://www.sananalytics.com/lab/twitter-sentiment/ [1]: https://www.aaai.org/ocs/index.php/SSS/SSS13/paper/download/5702/5909 https://www.aaai.org/ocs/index.php/SSS/SSS13/paper/download/...
- prabhatjha 8y ago
  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.
  - alexbeloi 8y ago
    >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.
    - prabhatjha 8y ago
      Got it. Yes, topic classification does not generalize across domains based on what we see in on our implementation.

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? ;-)

alexbeloi 8y ago

>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. [0]: http://www.sananalytics.com/lab/twitter-sentiment/ http://www.sananalytics.com/lab/twitter-sentiment/ [1]: https://www.aaai.org/ocs/index.php/SSS/SSS13/paper/download/5702/5909 https://www.aaai.org/ocs/index.php/SSS/SSS13/paper/download/...

prabhatjha 8y ago
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.
- alexbeloi 8y ago
  >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.
  - prabhatjha 8y ago
    Got it. Yes, topic classification does not generalize across domains based on what we see in on our implementation.