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

There are no magic "shortcuts" in sentiment analysis, as with any other sort of text analysis that seeks to discover the underlying "aboutness," of a chunk of text. Attempting to short cut proven text analysis methods through simplistic "adjective" checking or similar approaches leads to ambiguity, incorrect classification, etc., that at the end of the day give you a poor accuracy read on sentiment. The more terse the source (e.g. Twitter), the more difficult the problem.