Sentiment lexicons are language resources widely used in opinion mining and important tools in unsupervised sentiment classification. We present a comparative study of sentiment classification of reviews on six different domains using sentiment lexicons from different sources. Our results highlight the tendency of a lexicon's performance to be imbalanced towards one class, and indicate lexicon accuracy varies with the target domain. We propose an approach that combines information from different lexicons to make classification decisions and achieve more robust results that consistently improve our baseline across all domains tested. These are further refined by a domain independent score adjustment that mitigates the effect of the precision imbalance seen on some of the results.