Unsupervised Sentiment Analysis

Question

I\'ve been reading a lot of articles that explain the need for an initial set of texts that are classified as either \'positive\' or \'negative\' before a sentiment analysis

Answer 1

The paper of Turney (2002) mentioned by larsmans is a good basic one. In a newer research, Li and He [2009] introduce an approach using Latent Dirichlet Allocation (LDA) to train a model that can classify an article's overall sentiment and topic simultaneously in a totally unsupervised manner. The accuracy they achieve is 84.6%.