Open Access

ARTICLE

Multi-Level Knowledge Engineering Approach for Mapping Implicit Aspects to Explicit Aspects

Jibran Mir¹, Azhar Mahmood^2,*, Shaheen Khatoon³

1 Computer Science Department, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology (SZABIST), Faculty of Computing and Engineering Sciences, Islamabad, Pakistan
2 Department of Computer Science, Capital University of Science & Technology, Faculty of Computing, Islamabad
3 College of Computer Science and Information Technology, King Faisal University, Saudi Arabia

* Corresponding Author: Azhar Mahmood. Email:

(This article belongs to this Special Issue: Big Data Analytics and Artificial Intelligence Techniques for Complex Systems)

Computers, Materials & Continua 2022, 70(2), 3491-3509. https://doi.org/10.32604/cmc.2022.019952

Received 03 May 2021; Accepted 24 June 2021; Issue published 27 September 2021

Abstract

Aspect's extraction is a critical task in aspect-based sentiment analysis, including explicit and implicit aspects identification. While extensive research has identified explicit aspects, little effort has been put forward on implicit aspects extraction due to the complexity of the problem. Moreover, existing research on implicit aspect identification is widely carried out on product reviews targeting specific aspects while neglecting sentences’ dependency problems. Therefore, in this paper, a multi-level knowledge engineering approach for identifying implicit movie aspects is proposed. The proposed method first identifies explicit aspects using a variant of BiLSTM and CRF (Bidirectional Long Short Memory-Conditional Random Field), which serve as a memory to process dependent sentences to infer implicit aspects. It can identify implicit aspects from four types of sentences, including independent and three types of dependent sentences. The study is evaluated on a large movie reviews dataset with 50k examples. The experimental results showed that the explicit aspect identification method achieved 89% F1-score and implicit aspect extraction methods achieved 76% F1-score. In addition, the proposed approach also performs better than the state-of-the-art techniques (NMFIAD and ML-KB+) on the product review dataset, where it achieved 93% precision, 92% recall, and 93% F1-score.

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Keywords

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