  We propose a novel approach to generate aspect hierarchies that proved to be
consistently correct compared with human-generated hierarchies. We present an
unsupervised technique using Rhetorical Structure Theory and graph analysis. We
evaluated our approach based on 100,000 reviews from Amazon and achieved an
astonishing 80% coverage compared with human-generated hierarchies coded in
ConceptNet. The method could be easily extended with a sentiment analysis model
and used to describe sentiment on different levels of aspect granularity.
Hence, besides the flat aspect structure, we can differentiate between aspects
and describe if the charging aspect is related to battery or price.
