hrmm, not sure how that bodes in terms of SE ethics... but sure, assuming you mean only answers that legitimately warrant upvotes. i can look through a bit later.
we had to pick a topic, do a 30 minute presentation on it, and create a program that explores the topic. basically we have to find a few research papers, present a summary of them, and recreate a watered down version
well, typically IR focuses on retrieving documents based on search terms (think good)
but this is a bit of an AI/IR combo that focuses on taking documents (or in this case tweets)
determining who the speaker is, what the target is, and how the speaker feels about the target
for example, if someone tweets "@justinbieber is a loser!" then they obviously do not have very fond feelings towards justin bieber
there's a lot of ways to go about sentiment analysis. the approach i'm taking is an n-gram/neural net hybrid
an n-gram is just a collection of n words. so like "The dog ran" 1-grams would be the, dog and ran. 2-grams would be "the dog" and "dog ran". so on
a neural net is a computer simulation of an organic brain (sort of)
it has connected neurons that form a bit of a mesh. information goes in, data comes out. the complicated part is training the neural net to do what you want and do it accurately
@Corbin Interesting. So you are doing web mining on Twitter for the emotions that tweets display to people. And to do that you are interpreting each word individually to determine the emotion that is being conveyed.
i have a very unpleasant night ahead of me since i've gotten about 3 minutes worth of presentation, 10% of the programming part done, and it's due in 12 hours x.x
@@psp Hey psp. Would you mind coming to [chat]? Or at the very least, read the site faq as I believe you are not understanding the purpose of Stack Exchange. Thanks!