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 \u201call-in-one\u201d 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 \u201ckitchen sink\u201d approach, while technically non-glamorous, might be perfectly adequate in practice. We also show that the common anecdotal evidence for sentiment terms that \u201cflip\u201d polarity across domains is not borne out empirically.<\/p>\n","protected":false},"excerpt":{"rendered":"
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 \u201call-in-one\u201d classifier that utilizes all available training data from all but the target domain tends to outperform published domain adaptation methods. 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