This paper introduces the three SSIX corpora for sentiment analysis. These corpora address the need to provide annotated data for supervised learning methods. They focus on stock-market related messages extracted from two financial microblog platforms, i.e., StockTwits and Twitter. In total they include 2,886 messages with opinion targets. These messages are provided with polarity annotation set on a continuous scale by three or four experts in each language. The annotation information identifies the targets with a sentiment score. The annotation process includes manual annotation verified and consolidated by financial experts. The creation of the annotated corpora took into account principled sampling strategies as well as inter-annotator agreement before consolidation in order to maximize data quality