Mood wasn't exactly the focus, so it's robustness is limited - i.e. lots of room for improvement. The mood detection (based on my manual tagging) is roughly 80% accurate, which IMO was "good enough". It's more accurate as you get to the edges (very positive or very negative), the middle is kinda fuzzy, but that's kind of the nature of language.