Performance comparison ensemble classifier’s performance in answering frequently asked questions about psychology

Vy Thuy Tong; Hieu Chi Tran; Kiet Trung Tran

doi:10.46223/HCMCOUJS.tech.en.14.1.2921.2024

14(1)2024

Performance comparison ensemble classifier’s performance in answering frequently asked questions about psychology

Author - Affiliation:

Vy Thuy Tong - Ho Chi Minh City Open University, Ho Chi Minh City , Vietnam

Hieu Chi Tran - Ho Chi Minh City Open University, Ho Chi Minh City , Vietnam

Kiet Trung Tran - Ho Chi Minh City Open University, Ho Chi Minh City , Vietnam

Corresponding author: Kiet Trung Tran - kiet.tt@ou.edu.vn

DOI:10.46223/HCMCOUJS.tech.en.14.1.2921.2024

Submitted: 21-08-2023
Accepted: 07-02-2024
Published: 05-03-2024

Abstract

In today’s era of digital healthcare transformation, there is a growing demand for swift responses to mental health queries. To meet this need, we introduce an AI-driven chatbot system designed to automatically address frequently asked questions in psychology. Leveraging a range of classifiers including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naïve Bayes, our system extracts insights from expert data sources and employs natural language processing techniques like LDA Topic Modeling and Cosine similarity to generate contextually relevant responses. Through rigorous experimentation, we find that SVM surpasses Naïve Bayes and KNN in accuracy, precision, recall, and F1-score, making it our top choice for constructing the final response system. This research underscores the effectiveness of ensemble classifiers, particularly SVM, in providing accurate and valuable information to enhance mental health support in response to common psychological inquiries.

Keywords

classification; KNN; Naive Bayes; psychology; SVM

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Cite this paper as:

Tong, V. T., Tran, H. C., & Tran, K. T. (2024). Performance comparison ensemble classifier’s performance in answering frequently asked questions about psychology. Ho Chi Minh City Open University Journal of Science – Engineering and Technology, 14(1), 65-70. doi:10.46223/HCMCOUJS.tech.en.14.1.2921.2024

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DOI: https://doi.org/10.46223/HCMCOUJS.tech.en.14.1.2921.2024