Nowadays, many approaches for Sentiment Analysis (SA) rely on affective lexicons to identify emotions transmitted in opinions.However, most of these lexicons do not consider that a word can express different sentiments in different predication domains, introducing errors in the sentiment inference.Due to this problem, we present a model based on a context-graph which can be apac1/60/1/cw used for building domain specic sentiment moondrop quarks lexicons (DL: Dynamic Lexicons) by propagating the valence of a few seed words.
For different corpora, we compare the results of a simple rule-based sentiment classier using the corresponding DL, with the results obtained using a general affective lexicon.For most corpora containing specic domain opinions, the DL reaches better results than the general lexicon.