Opinion Mining in Online Reviews About Distance Education Programs
The popularity of distance education pro- grams is increasing at a fast pace. En par with this development, online communica- tion in fora, social media and reviewing platforms between students is increasing as well. Exploiting this information to sup- port 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 edu- cation experts with the goal to develop a dataset of reviews annotated with relevant categories and aspects in each category dis- cussed 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 as- pects under discussion in a review together with their sentiment. We frame this task as a multi-label hierarchical text classifi- cation problem and empirically investigate the performance of different classification architectures to couple the prediction of a category with the prediction of particu- lar aspects in this category. We evaluate different architectures and show that a hier- archical approach leads to superior results in comparison to a flat model which makes decisions independently.