Opinion Mining in Online Reviews About Distance Education Programs
This work addresses the need for automated analysis of student feedback to support students and institutions in evaluating distance education programs, but it is incremental as it applies existing hierarchical classification methods to a new domain.
The study tackled the problem of extracting opinions from online reviews about distance education programs by developing a dataset annotated with categories, aspects, and sentiment, and showed that a hierarchical classification approach outperforms a flat model.
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.