A novel wrapper feature selection algorithm based on iterated greedy metaheuristic for sentiment classification

Gökalp, Osman; Taşçı, Erdal; Uğur, Aybars

doi:10.1016/j.eswa.2020.113176

Cited by 104 publications

(50 citation statements)

References 21 publications

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“…They applied their algorithm to Hindi and English texts using SVM as the classifier. Gokalp et al [43] proposed another wrapperbased feature selection method for sentiment analysis. The proposed model is based on a Greedy Algorithm that utilizes six different filter-based metrics, including Chi-square and ReliefF, in the construction of the model.…”

Section: Related Workmentioning

confidence: 99%

Evolutionary Multiobjective Feature Selection for Sentiment Analysis

Deniz

Angin

2021

IEEE Access

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Section: Related Workmentioning

confidence: 99%

Evolutionary Multiobjective Feature Selection for Sentiment Analysis

Deniz

Angin

2021

IEEE Access

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“…An example for type (a): In these methods, different filtering criteria such as information gain and correlation 38,39 or the mutual information criterion 40 have been used to score and select the useful features. Mutual Information Features Selection (MIFS) is an algorithm for character rating 41 .…”

Section: Related Workmentioning

confidence: 99%

Toward text psychology analysis using social spider optimization algorithm

Hosseinalipour

Gharehchopogh

Màsdàrí

et al. 2021

Concurrency and Computation

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Different nature‐inspired meta‐heuristic algorithms have been proposed to solve optimization problems. One of these algorithms is called social spider optimization (SSO) algorithm. Spiders' natural behaviors have inspired them to find the bait position by detecting vibrations in their web. Although the SSO algorithm has good accuracy in achieving optimal solutions, it suffers from a low convergence rate. In this paper, we attempted to improve SSO by changing its motion and mating parameters. To provide a practical example of using the new proposed algorithm, we based it on multi‐objective opposition‐based SSO, named MOPSSO. We used this algorithm in a feature selection process for analyzing text psychology, which is a multi‐objective problem. Textual psychology analysis is used in various fields, including collecting and analyzing people's views on various products, topics, social and political events. After selecting features, in order to classify the text, we used a new hybrid method that hybrids fuzzy C‐MEANS data clustering technique, a decision tree (DT), and Naïve Bayes (NB). Experimental results show that the improved SSO algorithm performs better than SSO, social spider algorithm, and CMA‐ES algorithms. Additionally, the performance of the proposed hybrid classification method is better than those of NB and DT.

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“…Features selection techniques are primarily considered into filter [39] and Wrapper [40] approaches. The recent literature review presents the Wrapper approach as a better performer for sentiment classification as compared to the filter approach [41].…”

Section: Related Workmentioning

confidence: 99%

“…The recent literature review presents the Wrapper approach as a better performer for sentiment classification as compared to the filter approach [41]. For instance, Gokalp et al introduced a Wrapper based feature selection algorithm for SA [40]. Similarly, Al-Tashi et al [42] projected a multi-objective method for feature selection and reduction by employing the Wrapper based algorithm to assess the performance of selected features for classification.…”

Section: Related Workmentioning

confidence: 99%

GAWA–A Feature Selection Method for Hybrid Sentiment Classification

et al. 2020

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Sentiment analysis or opinion mining is the key to natural language processing for the extraction of useful information from the text documents of numerous sources. Several different techniques, i.e., simple rule-based to lexicon-based and more sophisticated machine learning algorithms, have been widely used with different classifiers to get the factual analysis of sentiment. However, lexicon-based sentiment classification is still suffering from low accuracies, mainly due to the deficiency of domain-oriented competitive dictionaries. Similarly, machine learning-based sentiment is also tackling the accuracy constraints because of feature ambiguity from social data. One of the best ways to deal with the accuracy issue is to select the best feature-set and reduce the volume of the feature. This paper proposes a method (namely, GAWA) for feature selection by utilizing the Wrapper Approaches (WA) to select the premier features and the Genetic Algorithm (GA) to reduce the size of the premier features. The novelty of this work is the modified fitness function of heuristic GA to compute the optimal features by reducing the redundancy for better accuracy. This work aims to present a comprehensive model of hybrid sentiment by using the proposed method, GAWA. It will be valued in developing a new approach for the selection of feature-set with a better accuracy level. The experiments revealed that these techniques could reduce the feature-set upto 61.95% without negotiating the accuracy level. The new optimal feature sets enhanced the efficiency of the Naïve Bayes algorithm up to 92%. This work is compared with the conventional method of feature selection and concluded the 11% better accuracy than PCA and 8% better than PSO. Furthermore, the results are compared with the literature work and found that the proposed method outperformed the previous research.

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A novel wrapper feature selection algorithm based on iterated greedy metaheuristic for sentiment classification

Cited by 104 publications

References 21 publications

Evolutionary Multiobjective Feature Selection for Sentiment Analysis

Evolutionary Multiobjective Feature Selection for Sentiment Analysis

Toward text psychology analysis using social spider optimization algorithm

GAWA–A Feature Selection Method for Hybrid Sentiment Classification

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