In this paper, we introduce a novel comprehensive dataset of 7,992 German tweets, which were manually annotated by two human experts with fine-grained opinion relations. A rich annotation scheme used for this corpus includes such sentiment-relevant elements as opinion spans, their respective sources and targets, emotionally laden terms with their possible contextual negations and modifiers. Various inter-annotator agreement studies, which were carried out at different stages of work on these data (at the initial training phase, upon an adjudication step, and after the final annotation run), reveal that labeling evaluative judgements in microblogs is an inherently difficult task even for professional coders. These difficulties, however, can be alleviated by letting the annotators revise each other’s decisions. Once rechecked, the experts can proceed with the annotation of further messages, staying at a fairly high level of agreement.

Despite a substantial progress made in developing new sentiment lexicon generation (SLG) methods for English, the task of transferring these approaches to other languages and domains in a sound way still remains open. In this paper, we contribute to the solution of this problem by systematically comparing semi-automatic translations of common English polarity lists with the results of the original automatic SLG algorithms, which were applied directly to German data. We evaluate these lexicons on a corpus of 7,992 manually annotated tweets. In addition to that, we also collate the results of dictionary- and corpus-based SLG methods in order to find out which of these paradigms is better suited for the inherently noisy domain of social media. Our experiments show that semi-automatic translations notably outperform automatic systems (reaching a macro-averaged F1-score of 0.589), and that dictionary-based techniques produce much better polarity lists as compared to corpus-based approaches (whose best F1-scores run up to 0.479 and 0.419 respectively) even for the non-standard Twitter genre.