Self-Configuring Knowledge Graph-Based BiLSTM Classifier with Bat–Harris Optimization for Sentiment Lexicon

Sentiment Lexicon (SL) is utilized to extract feedback from a large amount of data, which encompass numerous words, in which a few words contain various semantic definitions in various fields, which is called domain-specific (DS) language, and every word under its domain is demonstrated very significantly as their definition varies from one another. Therefore, this research proposes a Self-configuring knowledge graph-based BiLSTM Classifier with Bat–Harris optimization to determine the accurate sentiment from the comment or text message as well as compute whether it is positive, negative, or neutral, by utilizing the feature extraction process which includes Term Frequency-Inverse Document Frequency (TF-IDF), as well as Hybrid word2vec features, and finally detect the sentiment polarity of text. The experimental results which are depending on the performance metrics show that the developed model is improved compared to the prior method, in which the rate of accuracy of the developed scheme on TP 90 is 93.40% and 94.29%, whereas the sensitivity attains the value of 93.31%, and 94.20%, and finally, the specificity acquires the value of 93.68%, and 94.66% based on datasets 1 and 2.