Swarming Behavior of Harris Hawks Optimizer for Arabic Opinion Mining

Abstract

At present, the immense development of social networks allows generating a significant amount of textual data, which has facilitated researchers to explore the field of opinion mining. In addition, the processing of textual opinions based on the term frequency-inverse document frequency method gives rise to a dimensionality problem. This study aims to detect the nature of opinions in the Arabic language employing a swarm intelligence (SI)-based algorithm, Harris hawks algorithm, to select the most relevant terms. The experimental study has been tested on two datasets: Arabic JordanianGeneral Tweets and Opinion Corpus for Arabic. In terms of accuracy and number of features, the results are better than those of other SI based algorithms, such as grey wolf optimizer and grasshopper optimization algorithm, and other algorithms in the literature, such as differential evolution, genetic algorithm, particle swarm optimization, basic and enhanced whale optimizer algorithm, slap swarm algorithm, and ant-lion optimizer.