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.

中文翻译：

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使用社交蜘蛛优化算法进行文本心理分析

已经提出了不同的受自然启发的元启发式算法来解决优化问题。其中一种算法称为社交蜘蛛优化 (SSO) 算法。蜘蛛的自然行为激发了它们通过检测网中的振动来找到诱饵位置。尽管 SSO 算法在获得最优解方面具有良好的准确性，但它的收敛速度较低。在本文中，我们试图通过改变其运动和交配参数来改进 SSO。为了提供使用新提出的算法的实际示例，我们将其基于多目标反对的 SSO，称为 MOPSSO。我们在特征选择过程中使用该算法来分析文本心理，这是一个多目标问题。文本心理分析用于各个领域，包括收集和分析人们的 对各种产品、话题、社会和政治事件的看法。在选择特征后，为了对文本进行分类，我们使用了一种新的混合方法，该方法混合了模糊 C-MEANS 数据聚类技术、决策树 (DT) 和朴素贝叶斯 (NB)。实验结果表明，改进的SSO算法性能优于SSO、社交蜘蛛算法和CMA-ES算法。此外，所提出的混合分类方法的性能优于 NB 和 DT。和 CMA-ES 算法。此外，所提出的混合分类方法的性能优于 NB 和 DT。和 CMA-ES 算法。此外，所提出的混合分类方法的性能优于 NB 和 DT。