An Improved African Vulture Optimization Algorithm for Feature Selection Problems and Its Application of Sentiment Analysis on Movie Reviews

Abstract

The African vulture optimization algorithm (AVOA) is inspired by African vultures’ feeding and orienting behaviors. It comprises powerful operators while maintaining the balance of exploration and efficiency in solving optimization problems. To be used in discrete applications, this algorithm needs to be discretized. This paper introduces two versions based on the S-shaped and V-shaped transfer functions of AVOA and BAOVAH. Moreover, the increase in computational complexity is avoided. Disruption operator and Bitwise strategy have also been used to maximize this model’s performance. A multi-strategy version of the AVOA called BAVOA-v1 is presented. In the proposed approach, i.e., BAVOA-v1, different strategies such as IPRS, mutation neighborhood search strategy (MNSS) (balance between exploration and exploitation), multi-parent crossover (increasing exploitation), and Bitwise (increasing diversity and exploration) are used to provide solutions with greater variety and to assure the quality of solutions. The proposed methods are evaluated on 30 UCI datasets with different dimensions. The simulation results showed that the proposed BAOVAH algorithm performed better than other binary meta-heuristic algorithms. So that the proposed BAOVAH algorithm set is the most accurate in 67% of the data set, and 93% of the data set is the best value of the fitness functions. In terms of feature selection, it has shown high performance. Finally, the proposed method in a case study to determine the number of neurons and the activator function to improve deep learning results was used in the sentiment analysis of movie viewers. In this paper, the CNNEM model is designed. The results of experiments on three datasets of sentiment analysis—IMDB, Amazon, and Yelp—show that the BAOVAH algorithm increases the accuracy of the CNNEM network in the IMDB dataset by 6%, the Amazon dataset by 33%, and the Yelp dataset by 30%.

An Improved hybrid Salp Swarm Optimization and African Vulture Optimization Algorithm for Global Optimization Problems and Its Applications in Stock Market Prediction

“…The results obtained from these tests are shown in tables (10 to 15). The p-values obtained from the Wilcoxon rank-sum statistical test are shown in tables (10)(11)(12)(13). According to the values shown in Table (10), the AVOA-SSA algorithm in F01 to F13 functions performed significantly better in almost all cases when compared to other optimization algorithms.…”

A multi-strategy enhanced African vultures optimization algorithm for global optimization problems

“…Another two binary versions of AVOA have been proposed to solve the feature selection (FS) problem using two transfer functions (S and V shaped; Balakrishnan et al, 2022). Other work has been done to solve the FS problem and sentiment analysis on movie reviews (Shaddeli et al, 2022). Also, an ameliorated AVOA version has been suggested to diagnose the defects of the rolling bearing.…”

An improved Arabic text classification method using word embedding

“…Irrelevant features contain no interesting information on the topic of classification, whereas redundant features contain information that already exists in more useful features [3]. Common feature selection approaches in text classification include sentiment analysis [4]- [6], text classification [7], [8], image retrieval [9], and more. It is possible to select the most compelling features from the original datasets using a variety of feature selection approaches.…”