Toward text psychology analysis using social spider optimization algorithm

Abstract

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.

A Quantum Language-Inspired Tree Structural Text Representation for Semantic Analysis

“…The neural network-based methods use the attention mechanism to assign different semantic weights to words with good experimental results in many downstream tasks, such as LSTM [6], BiLSTM [7] and BERT [8]. Semantic analysis based on attention mechanism has been involved in many works [9][10][11] and can reflect the different weights of words in different texts. The attention mechanism is introduced to obtain different weight of words in order to extract enough key information.…”