In this paper we undertake a large cross-domain investigation of sentiment domain adaptation, challenging the practical necessity of sentiment domain adaptation algorithms. We first show that across a wide set of domains, a simple “all-in-one” classifier that utilizes all available training data from all but the target domain tends to outperform published domain adaptation methods. A very simple ensemble classifier also performs well in these scenarios. Combined with the fact that labeled data nowadays is inexpensive to come by, the “kitchen sink” approach, while technically non-glamorous, might be perfectly adequate in practice. We also show that the common anecdotal evidence for sentiment terms that “flip” polarity across domains is not borne out empirically.