  The popularity of distance education programs is increasing at a fast pace.
En par with this development, online communication in fora, social media and
reviewing platforms between students is increasing as well. Exploiting this
information to support fellow students or institutions requires to extract the
relevant opinions in order to automatically generate reports providing an
overview of pros and cons of different distance education programs. We report
on an experiment involving distance education experts with the goal to develop
a dataset of reviews annotated with relevant categories and aspects in each
category discussed in the specific review together with an indication of the
sentiment.
  Based on this experiment, we present an approach to extract general
categories and specific aspects under discussion in a review together with
their sentiment. We frame this task as a multi-label hierarchical text
classification problem and empirically investigate the performance of different
classification architectures to couple the prediction of a category with the
prediction of particular aspects in this category. We evaluate different
architectures and show that a hierarchical approach leads to superior results
in comparison to a flat model which makes decisions independently.
