  Providing opinions through labeling of images, tweets, etc. have drawn
immense interest in crowdsourcing markets. This invokes a major challenge of
aggregating multiple opinions received from different crowd workers for
deriving the final judgment. Generally, opinion aggregation models deal with
independent opinions, which are given unanimously and are not visible to all.
However, in many real-life cases, it is required to make the opinions public as
soon as they are received. This makes the opinions dependent and might
incorporate some bias. In this paper, we address a novel problem, hereafter
denoted as dependent judgment analysis, and discuss the requirements for
developing an appropriate model to deal with this problem. The challenge
remains to be improving the consensus by revealing true opinions.
