  Sentiment analysis is often a crowdsourcing task prone to subjective labels
given by many annotators. It is not yet fully understood how the annotation
bias of each annotator can be modeled correctly with state-of-the-art methods.
However, resolving annotator bias precisely and reliably is the key to
understand annotators' labeling behavior and to successfully resolve
corresponding individual misconceptions and wrongdoings regarding the
annotation task. Our contribution is an explanation and improvement for precise
neural end-to-end bias modeling and ground truth estimation, which reduces an
undesired mismatch in that regard of the existing state-of-the-art.
Classification experiments show that it has potential to improve accuracy in
cases where each sample is annotated only by one single annotator. We provide
the whole source code publicly and release an own domain-specific sentiment
dataset containing 10,000 sentences discussing organic food products. These are
crawled from social media and are singly labeled by 10 non-expert annotators.
