Improved whale optimization algorithm for feature selection in Arabic sentiment analysis

Tubishat, Mohammad; Abushariah, Mohammad A. M.; Idris, Norisma; Aljarah, Ibrahim

doi:10.1007/s10489-018-1334-8

Cited by 207 publications

(79 citation statements)

References 60 publications

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“…[21] used the Genetic Algorithm(GA) for the FS problem. [4,22] improved Whale Optimization Algorithm (WOA) for the FS problem. [23] improved Ant Lion Optimizer for the FS problem.…”

Section: Related Workmentioning

confidence: 99%

“…In the literature, there are several studies that applied optimisation algorithms for feature selection problem. For example, [3,4] improved whale optimisation algorithm (WOA) and used it for feature selection, [5] improved particle swarm optimisation (PSO) and used it for feature selection, [6] used Grasshopper optimisation algorithm (GOA) for feature selection, [7] used Firefly algorithm (FFA) for feature selection, [8] used Differential Evolution (DE) for feature selection, [1] improved Salp Swarm Algorithm (SSA)and used it for feature selection, [9] used Genetic Algorithm (GA) for feature selection, [10] used Grey Wolf Optimization (GWO) for feature selection, [11] improved Gravitational Search algorithm (GSA) and used it for feature selection, [12] used fish swarm optimisation (FSO) for feature selection, [13] improved crow search algorithm (CSA) and used it for feature selection, [14] improved Dragonfly Algorithm (DA) and used it for feature selection, [15] improved social spider algorithm (SSA) and used it for feature selection, [1,16] improved Salp Swarm Algorithm (SSA) and used it for feature selection, [2] improved Harris hawks optimisation (HHO) and used it for feature selection.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Butterfly Optimization Algorithm for Feature Selection

et al. 2020

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Feature selection represents an essential pre-processing step for a wide range of Machine Learning approaches. Datasets typically contain irrelevant features that may negatively affect the classifier performance. A feature selector can reduce the number of these features and maximise the classifier accuracy. This paper proposes a Dynamic Butterfly Optimization Algorithm (DBOA) as an improved variant to Butterfly Optimization Algorithm (BOA) for feature selection problems. BOA represents one of the most recently proposed optimisation algorithms. BOA has demonstrated its ability to solve different types of problems with competitive results compared to other optimisation algorithms. However, the original BOA algorithm has problems when optimising high-dimensional problems. Such issues include stagnation into local optima and lacking solutions diversity during the optimisation process. To alleviate these weaknesses of the original BOA, two significant improvements are introduced in the original BOA: the development of a Local Search Algorithm Based on Mutation (LSAM) operator to avoid local optima problem and the use of LSAM to improve BOA solutions diversity. To demonstrate the efficiency and superiority of the proposed DBOA algorithm, 20 benchmark datasets from the UCI repository are employed. The classification accuracy, the fitness values, the number of selected features, the statistical results, and convergence curves are reported for DBOA and its competing algorithms. These results demonstrate that DBOA significantly outperforms the comparative algorithms on the majority of the used performance metrics. INDEX TERMS Butterfly optimisation algorithm, feature selection, local search algorithm based on mutation.

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“…[21] used the Genetic Algorithm(GA) for the FS problem. [4,22] improved Whale Optimization Algorithm (WOA) for the FS problem. [23] improved Ant Lion Optimizer for the FS problem.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic Butterfly Optimization Algorithm for Feature Selection

et al. 2020

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“…Abualigah ve diğerleri (2020) çevrimiçi dökümanların kullanılarak sağlık bakım kalitesinin arttırılması amacıyla bu alana özgü yapılan çalışmaları inceleyen bir derleme çalışma gerçekleştirmiştir [9]. Doğal dil işlemenin bir alt dalı olan duygu analizi, dilin gramatik yapısı ve öğe dizilimi gibi birçok alt problemle de uğraşmaktadır, bu açıdan Çince ve Arapça gibi dillere özgü farklı yaklaşımlar gerçekleştirilmesi gerekmektedir [10]. Yousif ve diğerleri (2017) bilimsel çalışmalarda benzerlik analiz yapılması, bilimsel atıflardaki duygu analizi yapılması için hibrit yaklaşımlar geliştirmişlerdir [11].…”

Section: Duygu Analiziunclassified

Derin Öğrenme ve Aşağı Örnekleme Yaklaşımları Kullanılarak Duygu Sınıflandırma Performansının İyileştirilmesi

Santur

2020

Fırat Üniversitesi Mühendislik Bilimleri Dergisi

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Bir metnin hangi duygu sınıfına ait olduğunu bulma problemi duygu sınıflandırma olarak bilinmektedir. Bu işlemin otomatize bir şekilde yapılması çevrimiçi ortamda büyük miktarda verinin çok kısa sürelerde analiz edilebilmesine olanak sağlamaktadır. Böylece müşteri memnuniyetini ölçme, reklam ve içerik önerme gibi birçok farklı amaçla kullanılabilmektedir. E-ticaret uygulamalarında duygu sınıflandırma için kullanıcı yorumlarının yanı sıra, memnuniyet derecesini ölçen sayısal bir puanlama ya da duygu durumunu kategorik bir değişken olarak ifade edecek bir değişkene daha ihtiyaç duyulmaktadır. Bu sayede etiketli verilerden oluşan veri seti üzerinde denetimli öğrenme ile model oluşturulmaktadır. Burada yaşanan bir dezavantaj kullanıcıların bir üründen çoğunlukla memnun olmaları ya da tam tersi şikâyetçi olmalarıdır. Bu durumda oluşan veri seti dengesiz olmaktadır. Bu çalışmada Türk e-ticaret platformu Hepsiburada firmasına ait 243 bin kullanıcı yorumundan oluşan veri seti kullanılmıştır. Dengesiz olan bu veri setinde, sınıflandırma performansının iyileştirilmesi için derin öğrenme algoritmaları kullanılmış ve dengesiz veri seti yaklaşımı sunulmuştur. Sunulan yaklaşım ile yanlış pozitif oranı % 69'dan % 90'a, doğruluk değeri ise % 95.5'ten % 99'a iyileştirilmiştir.

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“…Kumar & Khorwal, [18] employed the firefly algorithm for feature selection for decreasing the computational complexity and feature set size. Tubishat et al, [19] developed an improved whale optimization algorithm for feature selection in Arabic sentiment analysis. This approach utilized information gain for feature reduction and SVM for classification with high accuracy and less execution time.…”

Section: Related Workmentioning

confidence: 99%

Penguin Search Optimization Based Feature Selection for Automated Opinion Mining

Anuprathibha*¹,

Selvi²

2019

IJRTE

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Twitter sentiment analysis is a vital concept in determining the public opinions about products, services, events or personality. Analyzing the medical tweets on a specific topic can provide immense benefits in medical industry. However, the medical tweets require efficient feature selection approach to produce significantly accurate results. Penguin search optimization algorithm (PeSOA) has the ability to resolve NP-hard problems. This paper aims at developing an automated opinion mining framework by modeling the feature selection problem as NP-hard optimization problem and using PeSOA based feature selection approach to solve it. Initially, the medical tweets based on cancer and drugs keywords are extracted and pre-processed to filter the relevant informative tweets. Then the features are extracted based on the Natural Language Processing (NLP) concepts and the optimal features are selected using PeSOA whose results are fed as input to three baseline classifiers to achieve optimal and accurate sentiment classification. The experimental results obtained through MATLAB simulations on cancer and drug tweets using k-Nearest Neighbor (KNN), Naïve Bayes (NB) and Support Vector Machine (SVM) indicate that the proposed PeSOA feature selection based tweet opinion mining has improved the classification performance significantly. It shows that the PeSOA feature selection with the SVM classifier provides superior sentiment classification than the other classifiers

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Improved whale optimization algorithm for feature selection in Arabic sentiment analysis

Cited by 207 publications

References 60 publications

Dynamic Butterfly Optimization Algorithm for Feature Selection

Dynamic Butterfly Optimization Algorithm for Feature Selection

Derin Öğrenme ve Aşağı Örnekleme Yaklaşımları Kullanılarak Duygu Sınıflandırma Performansının İyileştirilmesi

Penguin Search Optimization Based Feature Selection for Automated Opinion Mining

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