In this paper, we systematically explore feature definition and selection strategies for sentiment polarity classification. We begin by exploring basic questions, such

as whether to use stemming, term frequency versus binary weighting, negation-enriched features, n-grams or

phrases. We then move onto more complex aspects

including feature selection using frequency-based vocabulary trimming, part-of-speech and lexicon selection (three types of lexicons), as well as using expected Mutual Information (MI). Using three product

and movie review datasets of various sizes, we show,

for example, that some techniques are more beneficial

for larger datasets than the smaller. A classifier trained

on only few features ranked high by MI outperformed

one trained on all features in large datasets, yet in small

dataset this did not prove to be true. Finally, we perform a space and computation cost analysis to further

understand the merits of various feature types.