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The popularity of distance education programs is increasing at a fast pace.En par with this development, online communication in fora, social media andreviewing platforms between students is increasing as well. Exploiting thisinformation to support fellow students or institutions requires to extract therelevant opinions in order to automatically generate reports providing anoverview of pros and cons of different distance education programs. We reporton an experiment involving distance education experts with the goal to developa dataset of reviews annotated with relevant categories and aspects in eachcategory discussed in the specific review together with an indication of thesentiment.  Based on this experiment, we present an approach to extract generalcategories and specific aspects under discussion in a review together withtheir sentiment. We frame this task as a multi-label hierarchical textclassification problem and empirically investigate the performance of differentclassification architectures to couple the prediction of a category with theprediction of particular aspects in this category. We evaluate differentarchitectures and show that a hierarchical approach leads to superior resultsin comparison to a flat model which makes decisions independently.

Opinion Mining in Online Reviews About Distance Education Programs