Just ran across this post from Matt Dickman who is Vice President, Digital Marketing at Fleishman-Hillard. His blog “Techno/Marketer” covers a myriad of subjects related to marketing and social media.
His take appears to be identical to ours — that detecting sentiment via traditional NLP is a challenge at best.
One of the most important aspects of online conversations is the sentiment of what the author is saying. Are they positive about you, negative or apathetic? The difference is vitally important, but very hard to determine due to the complexity of language.
We recently spoke with a senior VP at a major PR firm who echoed this. Apparently they have not found a viable alternative to hiring humans. Their next move is to test our process.
Here he describes Twistori.
It’s very cool to watch the service extract the terms and after a few minutes you see how difficult it is to get sentiment right.
We’re arguing that we do get sentiment “right,” and are happy to run sample sets of data through our system for prospective clients. If you’ve got hand scored data, we’d love to show you how we compare.
Sentimine is a product of 
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