Opinion Mining in Online Reviews About Distance Education Programs

TL;DR

This paper develops a hierarchical text classification approach to automatically extract opinions, categories, and sentiments from online reviews of distance education programs, aiding in summarizing pros and cons.

Contribution

It introduces a hierarchical classification method for extracting review categories, aspects, and sentiments, demonstrating improved performance over flat models.

Findings

Hierarchical models outperform flat models in accuracy.

Experimentally validated the approach with expert-annotated dataset.

Effective in extracting relevant opinions from online reviews.

Abstract

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.