  We estimate sentiment categories proportions for retrieval within large
retrieval sets. In general, estimates are produced by counting the
classification outcomes and then by adjusting such category sizes taking into
account misclassification error matrix. However, both the accuracy of the
classifier and the precision of the retrieval produce a large number of errors
that makes difficult the application of an aggregative approach to sentiment
analysis as a reliable and efficient estimation of proportions for sentiment
categories.
  The challenge for real time analytics during retrieval is thus to overcome
misclassification errors, and more importantly, to apply sentiment
classification or any other similar post-processing analytics at retrieval
time. We present a non-aggregative approach that can be applied to very large
retrieval sets of queries.
