Sentiment analysis in other languages?

You're confusing some things here. What you call a "classifying agent" is properly called a learner A learner takes a training set to produce a classifier That can be applied to unseen data to get positive/negative/neutral classifications.

You're confusing some things here. What you call a "classifying agent" is properly called a learner. A learner takes a training set to produce a classifier.

That can be applied to unseen data to get positive/negative/neutral classifications.To adapt a machine-learned NLP task such as this to a new language, you need a dataset to train on. Most sentiment analysis tools will require a labeled set, which can be expensive to make and hard to come by otherwise, so I suggest you check out the unsupervised method outlined in this answer (unsupervised = learns from unlabeled data). The method is described for English, but I've heard reasonable results have been achieved on other languages.

YMMV, though, based on the exact dataset you're going to use.

There is an idea to do the sentiment analysis of comments, basing on the youtube data. Notice that there is a like/dislike system on youtube, so you could probably use the likes/dislikes ratio on a video to assign some positive/negative value to the comments below it. Also, you can access the data through the gdata api.

And this method is independent on language, you just have to analyse videos published in chosen language.

I cant really gove you an answer,but what I can give you is a way to a solution, that is you have to find the anglde that you relate to or peaks your interest. A good paper is one that people get drawn into because it reaches them ln some way.As for me WW11 to me, I think of the holocaust and the effect it had on the survivors, their families and those who stood by and did nothing until it was too late.