I have read an excellent news story at the NY Times: Mining the Web for Feelings, Not Facts (via KDDNuggets), on what Opinion Mining/Sentiment Analysis is, its application to Social Networks and its increasing expexted impact on Web search. Technically is a very good dissemination article, the time it takes to be read is worth even if you have strong background on the topic.
It is not surprising that Twitter is considered a primary source of opinions. When I started a research project on things related to this some time ago, I quicky discovered that there is plenty of test collections in English, but much less in Spanish (this is a topic for other posts). I considered building a test collection in Spanish with Twitter search, surprisingly easy: type your product, brand, etc., collect tweets and evaluate them (a task for crowdsourcing, though). However, there is the problem of language (however, language identification is an easy Natural Language ProcessingTask, letter trigrams can give you over 99% accuracy on Western languages).
Si I have not become surprised with the applications that already do that (read in the article), search engines for Twitter with tweet polarity analysis. I have searched "Twitter" in them, with the following results ("+" means positive, "=" means neutral, "-" means negative) and screenshots:
Tweetfeel: + 65%, - 35% (over 350 tweets).
Twendz: + 34%, = 49%, - 17% (over an unknown number of tweets).
Twitrratr: + 11,50%, = 85,55%, - 2,95% (over 35561 tweets).
As the variety of tweets (however searched in the very same moment) is big, techniques are different, criteria also (e.g. no neutral at tweetfeel), it is not strange that results are quite different. There are some hints in the article..., but I recomend to follow the links in my previous post about the Opinion Mining tutorial by Bing Liu at WWWC 2008 for getting better informed about the techniques.