Political Optimizer with Probabilistic Neural Network-Based Arabic Comparative Opinion Mining

Opinion Mining (OM) studies in Arabic are limited though it is one of the most extensively-spoken languages worldwide. Though interest OM language growing among researchers, needs a vast number investigations due to unique morphological principles language. experience multiple challenges owing poor existence sources and Arabic-specific linguistic features. The comparative English wide novel. But, yet be established still nascent stage. features make essential expand regarding text. It contains such as diacritics, elongation, inflection word length. current study proposes Political Optimizer with Probabilistic Neural Network-based Comparative (POPNN-COM) model for proposed POPNN-COM aims recognize non-comparative texts context social media. Initially, involves different levels data pre-processing transform input into useful format. Then, pre-processed fed PNN classification recognition under class labels. At last, PO algorithm employed fine-tuning parameters involved this achieve enhanced results. was experimentally validated using two standard datasets, outcomes promising performance method over other recent approaches.