Multi-Granular Aspect Aggregation in Aspect-Based Sentiment Analysis

Abstract

Aspect-based sentiment analysis estimates the sentiment expressed for each particular aspect (e.g., battery, screen) of an entity (e.g., smartphone). Different words or phrases, however, may be used to refer to the same aspect, and similar aspects may need to be aggregated at coarser or finer granularities to fit the available space or satisfy user preferences. We introduce the problem of aspect aggregation at multiple granularities. We decompose it in two processing phases, to allow previous work on term similarity and hierarchical clustering to be reused. We show that the second phase, where aspects are clustered, is almost a solved problem, whereas further research is needed in the first phase, where semantic similarity measures are employed. We also introduce a novel sense pruning mechanism for WordNet-based similarity measures, which improves their performance in the first phase. Finally, we provide publicly available benchmark datasets.