Efficient Utilization of Dependency Pattern and Sequential Covering for Aspect Extraction Rule Learning

Abstract

The use of dependency rules for aspect extraction tasks in aspect-based sentiment analysis is a promising approach. One problem with this approach is incomplete rules. This paper presents an aspect extraction rule learning method that combines dependency rules with the Sequential Covering algorithm. Sequential Covering is known for its characteristics in constructing rules that increase positive examples covered and decrease negative ones. This property is vital to make sure that the rule set used has high performance, but not inevitably high coverage, which is a characteristic of the aspect extraction task. To test the new method, four datasets were used from four product domains and three baselines: Double Propagation, Aspectator, and a previous work by the authors. The results show that the proposed approach performed better than the three baseline methods for the F-measure metric, with the highest F-measure value at 0.633.