Multi Layered Rule-Based Technique for Explicit Aspect Extraction from Online Reviews

In the field of sentiment analysis, extracting aspects or opinion targets fromuser reviews about a product is a key task. Extracting the polarity of an opinion is much more useful if we also know the targeted Aspect or Feature. Rule based approaches, like dependency-based rules, are quite popular and effective for this purpose. However, they are heavily dependent on the authenticity of the employed parts-of-speech (POS) tagger and dependency parser. Another popular rule based approach is to use sequential rules, wherein the rules formulated by learning from the user's behavior. However, in general, the sequential rule-based approaches have poor generalization capability. Moreover, existing approaches mostly consider an aspect as a noun or noun phrase, so these approaches are unable to extract verb aspects. In this article, we have proposed a multi-layered rule-based (ML-RB) technique using the syntactic dependency parser based rules along with some selective sequential rules in separate layers to extract noun aspects. Additionally, after rigorous analysis, we have also constructed rules for the extraction of verb aspects. These verb rules primarily based on the association between verb and opinion words. The proposed multi-layer technique compensates for the weaknesses of individual layers and yields improved results on two publicly available customer review datasets. The F1 score for both the datasets are 0.90 and 0.88, respectively, which are better than existing approaches. These improved results can be attributed to the application of sequential/syntactic rules in a layered manner as well as the capability to extract both noun and verb aspects.

Automated summary

In sentiment analysis, extracting aspects or opinion targets from user reviews about a product is crucial. Rule-based approaches, such as dependency-based rules and sequential rules, have been widely used for this purpose. However, they heavily rely on the accuracy of POS tagger and dependency parser, and sequential rule-based approaches generally lack generalization capability. In this article, a multi-layered rule-based technique is proposed, which combines syntactic dependency parser based rules and selective sequential rules to extract noun aspects. Additionally, rules for extracting verb aspects are developed based on the association between verb and opinion words. The proposed technique improves the extraction results on two customer review datasets, achieving F1 scores of 0.90 and 0.88, respectively, surpassing existing approaches.