Year: 2025
Type: conference-paper
Abstract: Text classification problems, such as gender classification from a blog, have been a well-matured research area that has been well studied using machine learning algorithms. It has several application domains in market analysis, customer recommendation, and recommendation systems. This study presents a comparative analysis of the widely used machine learning algorithms, namely Support Vector Machines (SVM), Naïve Bayes (NB), Logistic Regression (LR), AdaBoost, XGBoost, and an SVM variant (SVM_R) with neuro-symbolic AI (NeSy). The paper also explores the effect of text representations such as TF–IDF, the Universal Sentence Encoder (USE), and RoBERTa. In addition, various feature extraction techniques, including Chi-Square, Mutual Information, and Principal Component Analysis, were discussed. Building on these, we introduce a comparative analysis of the machine learning and deep learning approaches in comparison to the NeSy. The experimental result shows that the use of the NeSy approach matched strong MLP results despite a limited dataset. The future work of this research will expand the knowledge base, the embedding types, and the hyperparameter configuration scope to further study the effectiveness of the NeSy approach.
Authors: Natnael Tilahun Sinshaw, Mengmei He, Tadesse K. Bahiru, Sudhir Kumar Mohapatra
Institutions: University of Houston, Sri Sri University
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Gender equality
Open Access status: green
OpenAlex ID: W4417337362
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