Best-Worst Scaling More Reliable than Rating Scales: A Case Study on Sentiment Intensity Annotation
This addresses annotation reliability issues for researchers and practitioners in natural language processing, but it is incremental as it builds on existing claims about BWS.
The study tackled the problem of inconsistent sentiment intensity annotations by comparing best-worst scaling (BWS) with rating scales, showing that BWS produces significantly more reliable results with the same number of annotations.
Rating scales are a widely used method for data annotation; however, they present several challenges, such as difficulty in maintaining inter- and intra-annotator consistency. Best-worst scaling (BWS) is an alternative method of annotation that is claimed to produce high-quality annotations while keeping the required number of annotations similar to that of rating scales. However, the veracity of this claim has never been systematically established. Here for the first time, we set up an experiment that directly compares the rating scale method with BWS. We show that with the same total number of annotations, BWS produces significantly more reliable results than the rating scale.