Different Aggregation Strategies for Generically Contextualized Sentiment Lexicons

Abstract. Sentiment detection has gained relevance in the last years due to the vast amount of publicly available opinion in the form of Web forums or blogs. Yet, it still suffers from the ambiguity of language, lowering the efficacy and accuracy of sentiment detection systems. Thus, it is important to also invoke context information to refine the initial values of sentiment terms. Moreover, domain-independence is desirable to avoid using a topic determination beforehand. This work investigates strategies for extracting non-generic features to be integrated into a socalled contextualized sentiment lexicon, capable of getting the context correctly and assigning sentiment terms the proper sentiment value. The proposed approach will be applied in an online-media aggregation and visualization portal, covering a vast number of news media sources.