Sentiment mining is a hot topic, with many possible uses, but coders can usually do something decent without any crazy magic.

The problems start when any grammar processing happens - because algorithms are then dependant of the language

(What about this service? I didn't find many details about it.)

Meanwhile, if you stay at the dictionary level, you can get decent results - that's what things such as the General Inquirer let you do - and you won't need for an API to do basic stemming of words and summing up the sentiment values from a database - see http://www.wjh.harvard.edu/~inquirer/

I've played with it - that's more or less a 1 day project if you use the existing database.

Bonus points if you add simple things - such as replacing the stemming algorithm by a Levenshtein distance or a phonex-style algorithm (typos, etc), or use inverse term frequency to mitigate the influence of "very sentimental" words that are falling into common use, i.e. whose current sentimental ponderation differs from the general inquirer ponderation.

I once played with sentiment analysis of mailing lists, to automatically get red flags when sh*t happens :-)