Sentiment classification is the task of getting a computer to decide if a sentence contains positive or negative words about the things it describes.
Sometimes we want the computer to be able to make predictions based on a sequence of inputs.
You'll need download the four data files and unzip them to a directory on your computer.
The previously thoroughly stupid computer that can't ever do anything right (and that needs to be laboriously told what to do via tedious procedural instruction) suddenly gets a voice of it's own and comes up with results that are not only a little bit interesting, but genuinely exciting! The computer still makes stupid mistakes for seemingly no reason. That means we do more important things like go to the beach while the computer trains and trains itself smarter. We know what we want and the computer figures out for itself the best way to deliver. At a simplistic level, traditional programming is about telling the computer what to do with inputs into a system to produce some desired output. Then it's up to the computer itself to decide the best way to do it. Instead of telling the computer exactly what it should do, we instead show it examples of what we would like done. Because it turns out to be very difficult to tell computers how to do complicated tasks. So when we want computers to make diagnoses from medical scans or translate text between multiple languages, machine learning is the only good tool that we have. But it turns out that computers can in fact manage to work out highly complex processes themselves (even if we might not really understand how they do it). For any non-trivial problem that you need to solve, machine learning might be able to figure out the best way to do it - without you needing to write any software yourself.Not writing software means that you can go to the beach while the computer works out the tricky parts of your process.
But That Would Be Bad (our goal is not to teach the computer the learning data, but rather to generalise on data it hasn't seen yet)