End-to-End Annotator Bias Approximation on Crowdsourced Single-Label Sentiment Analysis

TL;DR

This paper introduces an improved neural end-to-end method for modeling annotator bias in crowdsourced sentiment analysis, enhancing ground truth estimation and accuracy, especially with single-annotator labels, supported by a new dataset and open-source code.

Contribution

It presents a novel neural approach for precise annotator bias modeling and ground truth estimation, addressing limitations of existing methods in single-annotator scenarios.

Findings

Improved accuracy in sentiment classification with single annotations.

Effective bias modeling reduces label noise impact.

Open-source dataset and code facilitate further research.

Abstract

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.