Comparative analysis of machine and deep learning algorithms for semantic analysis in Iraqi dialect

Abstract

Text analytics, an essential component of artificial intelligence (AI) applications, plays a pivotal role in analyzing qualitative sentiments and responses in questionnaires, particularly for governmental and private organizations. Utilizing sentiment analysis enables a comprehensive understanding of people’s opinions, especially when expressed in lengthy texts in their native language, with minimal constraints. This study aims to identify the determinants of electronic service adoption among Iraqi citizens. A set of 1,695 questionnaires were distributed to Iraqi citizens; obtained 1,234 responses that were increased via data augmentation to 1,393 comments. Four machine learning (ML) and three deep learning (DL) algorithms Na¨ıve Bayes (NB), K-nearest neighboror machine (SVM), random forest (RF), as well as two variants of long-shortterm memory (LSTM) networks and convolutional neural networks (CNN) were employed to classify qualitative feedback. Following rigorous training and testing, the NB classification algorithm exhibited the highest accuracy, achieving 82.89%.