This contribution presents the preliminary version of SentiANNO, a sentiment-annotated corpus derived from sentences in historical Austrian newspapers, and details the annotation process and the training of annotators. The corpus is intended for fine-tuning existing Machine Learning models for the NLP (Natural Language Processing) task of Sentiment Analysis (SA), facilitating the automatic detection of emotions and opinions in texts (Liu, 2012). This corpus enables SA of journalistic texts in Austrian German from 1800 to 1938 and can be applied to historical texts across various topics.