Automatic creation of polarity lexicons is a crucial issue to be solved in order to reduce time and#R##N#efforts in the first steps of Sentiment Analysis. In this paper we present a methodology based on#R##N#linguistic cues that allows us to automatically discover, extract and label subjective adjectives#R##N#that should be collected in a domain-based polarity lexicon. For this purpose, we designed a#R##N#bootstrapping algorithm that, from a small set of seed polar adjectives, is capable to iteratively#R##N#identify, extract and annotate positive and negative adjectives. Additionally, the method#R##N#automatically creates lists of highly subjective elements that change their prior polarity even#R##N#within the same domain. The algorithm proposed reached a precision of 97.5% for positive#R##N#adjectives and 71.4% for negative ones in the semantic orientation identification task.
["Silvia V'azquez", 'Muntsa Padró', 'Núria Bel', 'Julio Gonzalo']
Automatic Extraction of Polar Adjectives for the Creation of Polarity Lexicons