Open Access

ARTICLE

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

by Mubashar Hussain¹, Toqir A. Rana^2,3, Aksam Iftikhar⁴, M. Usman Ashraf^5,*, Muhammad Waseem Iqbal⁶, Ahmed Alshaflut⁷, Abdullah Alourani⁸

1 School of Systems and Technology, University of Management and Technology, Lahore, 54770, Pakistan
2 Department of Computer Science and It, The University of Lahore, Lahore, 54000, Pakistan
3 School of Computer Sciences, Universiti Sains Malaysia (USM), Penang, 11800, Malaysia
4 Department of Computer Science, Comsats University Islamabad, Lahore, 54000, Pakistan
5 Department of Computer Science, GC Women University Sialkot, Pakistan
6 Department of Software Engineering, The Superior University, Lahore, 53700, Pakistan
7 Faculty of Computing and It, Albaha University, Albaha, Saudi Arabia
8 Department of Computer Science, Majmaah University, Saudi Arabia

* Corresponding Author: M. Usman Ashraf. Email:

Computers, Materials & Continua 2022, 73(3), 4641-4656. https://doi.org/10.32604/cmc.2022.024759

Received 30 October 2021; Accepted 23 March 2022; Issue published 28 July 2022

Abstract

In the field of sentiment analysis, extracting aspects or opinion targets from user 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.

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Keywords

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