Integrated Two Variant Deep Learners for Aspect-Based Sentiment Analysis: An Improved Meta-Heuristic-Based Model

Abstract

Several traditional methods were tested on standard datasets to evaluate client emotions transmitted via internet portals. Customers, on the other hand, continue to have difficulty obtaining aspect-oriented viewpoints voiced by other customers, and the accuracy of the current model is insufficient. The suggested Aspect-Based Sentimental Analysis (ABSA) starts with pre-processing, which includes “stop word and punctuation removal, lower case conversion, and stemming.” Aspect extraction, which entails dividing the nouns and adjectives, as well as verbs and adverbs, is the following step. The weighted polarity features from the “Vader sentiment intensity analyzer, as well as the word2vector and Term Frequency-Inverse Document Frequency (TF-IDF)” are concatenated. OIDL stands for Optimized Integrated Deep Learning, which combines two types of deep learners. The first is the combination of concatenated features with “Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN),” while the second is the combination of concatenated features with RNN. The Improved Coyote Optimization Algorithm (ICOA) improves both deep learners, and the conclusion of sentiment analysis result is considered both models. Thus, the suggested model surpasses standard methodologies regarding precision and accuracy, according to the results of the experiments.