  Sentiment analysis of microblogs such as Twitter has recently gained a fair
amount of attention. One of the simplest sentiment analysis approaches compares
the words of a posting against a labeled word list, where each word has been
scored for valence, -- a 'sentiment lexicon' or 'affective word lists'. There
exist several affective word lists, e.g., ANEW (Affective Norms for English
Words) developed before the advent of microblogging and sentiment analysis. I
wanted to examine how well ANEW and other word lists performs for the detection
of sentiment strength in microblog posts in comparison with a new word list
specifically constructed for microblogs. I used manually labeled postings from
Twitter scored for sentiment. Using a simple word matching I show that the new
word list may perform better than ANEW, though not as good as the more
elaborate approach found in SentiStrength.
