“Sentiment analysis” sounds like a solved classification problem, and the demos support that impression. The difficulty is not in the model. It is that the label is underspecified, the phenomena that break it are structural, and the accuracy you see on a public dataset does not transfer to your inbox.
Three tasks under one name
Before choosing a method, notice which of these you actually want, because they have different labels and different ceilings.
Document polarity. Is this review positive or negative? The task Pang, Lee and Vaithyanathan defined in Thumbs up? (EMNLP 2002) using movie reviews, and the one Stanford Sentiment Treebank (Socher et al., EMNLP 2013) refined with phrase-level annotations. Easiest of the three, and rarely the one a business needs.
Aspect-based sentiment. What does this text say about the battery, the shipping and the price, separately? Defined by SemEval-2014 Task 4. This is almost always what a product team means, and it is a different problem: the answer is a set of pairs, not a label.
