Extended rule-based opinion target extraction with a novel text pre-processing method and ensemble learning

Abstract

Opinion target extraction (OTE) is the extraction of explicit expressions related to entity aspects interpreted with subjective attributive words in the review sentences using supervised or rule-based approaches. Despite the constraints of syntactic-based relation rules, rule-based approaches can be domain-independently implemented. Although supervised approaches yield better results, more costly due to requiring a large number of labeled samples. This study proposes an unsupervised (rule-based) OTE approach with novel methods and extended rule-based techniques to overcome the aforementioned issues. In this study, first, a novel pattern-based text pre-processing method is proposed to eliminate punctuations that are incompatible with determinative group rules patterns. Then, implemented syntactic-based relation rules on the dependency relation graph are extended with new auxiliary features to extract multi-word expressions which modify each other. The majority voting method is used for optimizing the performance of outputs. Finally, the effectiveness of the proposed approach was tested on a restaurant review dataset. The experimental results show that the proposed approach outperforms all unsupervised approaches. Additionally, it gives comparable results with the supervised approaches, revealing the effectiveness of the proposed approach.