Explicit aspects extraction in sentiment analysis using optimal rules combination

Aspect extraction represents a core task of aspect-based sentiment analysis. This study presents a supervised aspect extraction algorithm for explicit aspect extraction from formal and informal texts. To accomplish the new algorithm, 126 aspect extraction rules are combined to cover both formal and...

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Published in:Future generation computer systems Vol. 114; pp. 448 - 480
Main Authors: Tubishat, Mohammad, Idris, Norisma, Abushariah, Mohammad
Format: Journal Article
Language:English
Published: Elsevier B.V 01.01.2021
Subjects:
Dependency-based rules
Explicit aspect extraction
Improved whale optimization algorithm
Pattern-based rules
Sentiment analysis
Improved whale optimization algorithm
Sentiment analysis
Pattern-based rules
Dependency-based rules
Explicit aspect extraction
ISSN:0167-739X, 1872-7115
Online Access:Get full text
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Abstract Aspect extraction represents a core task of aspect-based sentiment analysis. This study presents a supervised aspect extraction algorithm for explicit aspect extraction from formal and informal texts. To accomplish the new algorithm, 126 aspect extraction rules are combined to cover both formal and informal texts, because customer reviews are a mix of formal and informal texts. These 126 rules include certain dependency-based rules and pattern-based rules from previous studies, in addition to newly developed rules intended to overcome prior rules’ weaknesses. In addition, many aspect extraction rules have remained unexplored by previous studies. However, many of these 126 rules are irrelevant and should be removed. Thus, a prober selection of the included rules is required. Therefore, in this study we also improved the Whale Optimization Algorithm (WOA) to address rules selection problem with an improved algorithm called improved WOA (IWOA). Two major improvements were included into IWOA. The first improvement is the development of a new update equation based on Cauchy mutation to improve WOA population diversity. The second improvement is the development of a new local search algorithm (LSA) to solve WOA local optima. The IWOA algorithm is applied on the full set of rules to select best rules subset and remove low quality rules. Finally, a new pruning algorithm (PA) has been developed to remove incorrect aspects and retain correct aspects. The Results on seven benchmark datasets demonstrate that IWOA+PA outperforms all other state-of-the-art baseline works and most recent works. •Development of new aspects extraction rules for sentiment analysis at aspect level.•The combination of different aspects extraction rules types.•An Improved Whale Optimization Algorithm (IWOA) for rules selection.•The development of aspect pruning algorithm.
AbstractList Aspect extraction represents a core task of aspect-based sentiment analysis. This study presents a supervised aspect extraction algorithm for explicit aspect extraction from formal and informal texts. To accomplish the new algorithm, 126 aspect extraction rules are combined to cover both formal and informal texts, because customer reviews are a mix of formal and informal texts. These 126 rules include certain dependency-based rules and pattern-based rules from previous studies, in addition to newly developed rules intended to overcome prior rules’ weaknesses. In addition, many aspect extraction rules have remained unexplored by previous studies. However, many of these 126 rules are irrelevant and should be removed. Thus, a prober selection of the included rules is required. Therefore, in this study we also improved the Whale Optimization Algorithm (WOA) to address rules selection problem with an improved algorithm called improved WOA (IWOA). Two major improvements were included into IWOA. The first improvement is the development of a new update equation based on Cauchy mutation to improve WOA population diversity. The second improvement is the development of a new local search algorithm (LSA) to solve WOA local optima. The IWOA algorithm is applied on the full set of rules to select best rules subset and remove low quality rules. Finally, a new pruning algorithm (PA) has been developed to remove incorrect aspects and retain correct aspects. The Results on seven benchmark datasets demonstrate that IWOA+PA outperforms all other state-of-the-art baseline works and most recent works. •Development of new aspects extraction rules for sentiment analysis at aspect level.•The combination of different aspects extraction rules types.•An Improved Whale Optimization Algorithm (IWOA) for rules selection.•The development of aspect pruning algorithm.
Author Abushariah, Mohammad
Tubishat, Mohammad
Idris, Norisma
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– sequence: 2
  givenname: Norisma
  surname: Idris
  fullname: Idris, Norisma
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  givenname: Mohammad
  surname: Abushariah
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  email: m.abushariah@ju.edu.jo
  organization: Computer Information Systems Department, King Abdullah II School of Information Technology, The University of Jordan, Amman, Jordan
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Keywords Improved whale optimization algorithm
Sentiment analysis
Pattern-based rules
Dependency-based rules
Explicit aspect extraction
Language English
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Snippet Aspect extraction represents a core task of aspect-based sentiment analysis. This study presents a supervised aspect extraction algorithm for explicit aspect...
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StartPage 448
SubjectTerms Dependency-based rules
Explicit aspect extraction
Improved whale optimization algorithm
Pattern-based rules
Sentiment analysis
Title Explicit aspects extraction in sentiment analysis using optimal rules combination
URI https://dx.doi.org/10.1016/j.future.2020.08.019
Volume 114
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