We propose a novel approach to sentiment analysis for a low resource setting.The intuition behind this work is that sentiment expressed towards an entity, targeted sentiment, may be viewed as a span of sentiment expressed across the entity.This representation allows us to model sentiment detection as a sequence tagging problem, jointly discovering people and organizations along with whether there is sentiment directed towards them.We compare performance in both Spanish and English on microblog data, using only a sentiment lexicon as an external resource.By leveraging linguisticallyinformed features within conditional random fields (CRFs) trained to minimize empirical risk, our best models in Spanish significantly outperform a strong baseline, and reach around 90% accuracy on the combined task of named entity recognition and sentiment prediction.Our models in English, trained on a much smaller dataset, are not yet statistically significant against their baselines.