An Optimization-based Learning Black Widow Optimization Algorithm for Text Psychology

محورهای موضوعی : Evolutionary Computing

Ali Hosseinalipour ¹ ( Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, IRAN )
Farhad Soleimanian Gharehchopogh ² ( Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, IRAN )
mohammad masdari ³ ( Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran. )
ALi Khademi ⁴ ( Department of Psychology Science, Urmia Branch, Islamic Azad University, Urmia, IRAN. )

تاریخ دریافت : 1399/10/14 تاریخ پذیرش : 1400/06/27 تاریخ انتشار : 1399/11/13

کلید واژه: black widow optimization algorithm, Meta-Heuristic Algorithm, text psychology, Feature Selection,

چکیده مقاله :

In recent years, social networks' growth has led to an increase in these networks' content. Therefore, text mining methods became important. As part of text mining, Sentiment analysis means finding the author's perspective on a particular topic. Social networks allow users to express their opinions and use others' opinions in other people's opinions to make decisions. Since the comments are in the form of text and reading them is time-consuming. Therefore, it is essential to provide methods that can provide us with this knowledge usefully. Black Widow Optimization (BWO) is inspired by black widow spiders' unique mating behavior. This method involves an exclusive stage, namely, cannibalism. For this reason, at this stage, species with an inappropriate evaluation function are removed from the circle, thus leading to premature convergence. In this paper, we first introduced the BWO algorithm into a binary algorithm to solving discrete problems. Then, to reach the optimal answer quickly, we base its inputs on the opposition. Finally, to use the algorithm in the property selection problem, which is a multi-objective problem, we convert the algorithm into a multi-objective algorithm. The 23 well-known functions were evaluated to evaluate the performance of the proposed method, and good results were obtained. Also, in evaluating the practical example, the proposed method was applied to several emotion datasets, and the results indicate that the proposed method works very well in the psychology of texts.

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