You are currently browsing the tag archive for the ‘election machine learning prediction president’ tag.

It’s an interesting thought. The upcoming Sentiment Analysis Symposium is having participants compete to try and do just that. But Sentiment Analysis would be only one tool out of a whole bag of NLP tricks that professionals in this field could use to try and figure out who will be our next president. The thing that strikes me about the whole idea is the thing that usually jumps out at me when asked about predictive tasks of this kind: what’s the source data and how much do you know about it? The answer – for me at least – is that I don’t know nearly enough to have any chance of success at this task (well beyond the coin-toss chance of anyone else). Just imagining the number of possibilities boggles the mind. Let’s say I wanted to make this a linear-sequence problem – similar to part-of-speech tagging. I could take sequences of rebuplicans-in-office followed by democrats, democrats-in-office followed by democrats……you get the idea. But, ooops….can’t really do that because, well, we just haven’t had enough presidents to make a decently robust NLP problem out of this. OK, back to the drawing board. How about I analyze the convention-night speeches of all the winners in american history vs. all the losers. OK, now I am on to something. I am sure to have a few million words and I can take tf-idf scores, topic profiles and the like that I can use to train up a nice winner-loser classifier. Why, it’ll be better than a spam filter. Trouble is, what I am more likely to get out of it is author-recognition rather than winner recognition unless I can take out all the words that are stylistically associated with the speakers. I could do that but that would be very time consuming….probably take a few months for me to find a bunch of speeches from each candidate and….well, the election would be over. So, maybe all these professional polling folks have it right. Maybe the best way to predict who the next president is going to be is to ask people who they are voting for. Maybe nobody really listens to these politicians anyway no matter what they say. And therein lies the trouble….and the reason this is one problem NLP might not lick. Doesn’t mean I won’t try just for fun.