Dependency Structure-Based Rules Using Root Node Technique for Explicit Aspect Extraction From Online Reviews

doi:10.1109/ACCESS.2023.3287830

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Dependency Structure-Based Rules Using Root Node Technique for Explicit Aspect Extraction From Online Reviews

2023-01-01

Pallavi Mishra

Sandeep Kumar Panda *

DOI：10.1109/ACCESS.2023.3287830

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Abstract

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Aspect-based sentiment analysis (ABSA) includes aspect extraction and sentiment analysis on those extracted aspects. This paper is mainly focused on the explicit aspect extraction from product based online customer reviews. Many research studies on explicit aspect extractions have adopted dependency rule-based techniques but limited their focus to only nouns and noun phrases as potential aspects. Moreover, extraction of multiple-aspects and multi-word aspects from online reviews are also not addressed by previous research studies. In this paper, we have proposed a Dependency structure-based rules using ROOT Node (DS-RN) technique using spaCy dependency parser to extract nouns, noun phrases, verbs, verb phrases in addition to single word, multiple aspects and multi word aspect extractions from customer review datasets. In our proposed methodology, 30 new dependency-based rules are formulated and implemented on 5 different product-based datasets. This study is based on the pattern analysis of dependency structures of review sentences to develop dependency-based rules for explicit aspect extraction. The proposed approach also incorporated lexicon-based pruning techniques to remove irrelevant aspects and retain correct aspects. The performance results on 5 different product-based customer review datasets demonstrate that our proposed DS-RN approach outperforms all other state-of-the-art baseline works with averaged value of precision as 87%, recall with 97% and 91% as F1-score.

Keywords:

Sentiment analysis

Feature extraction

Task analysis

Batteries

Electronic commerce

Whale optimization algorithms

Periodic structures

Aspects

dependency structure-based rule

explicit aspect extraction

opinion lexicon