Automatic creation of polarity lexicons is a crucial issue to be solved in order to reduce time and/nefforts in the first steps of Sentiment Analysis. In this paper we present a methodology based on/nlinguistic cues that allows us to automatically discover, extract and label subjective adjectives/nthat should be collected in a domain-based polarity lexicon. For this purpose, we designed a/nbootstrapping algorithm that, from a small set of seed polar adjectives, is capable to iteratively/nidentify, extract and annotate positive and negative adjectives. Additionally, the method/nautomatically creates lists of highly subjective elements that change their prior polarity even/nwithin the same domain. The algorithm proposed reached a precision of 97.5% for positive/nadjectives and 71.4% for negative ones in the semantic orientation identification task.