International Journal of Education and Management Engineering (IJEME)
IJEME Vol. 15, No. 1, 8 Feb. 2025
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Analysis of Human Behavior and Interests Based on Text Data
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Author(s)
Irada Alakbarova
1,*
Index Terms
Human Behavior, Machine Learning, TF-IDF, SVM, Demographic Data
Abstract
Information technology has revolutionized data collection and analysis, offering unprecedented opportunities to study human behavior. Various information registers, the internet of things, and electronic demographic platforms that collect and analyze user data from various online sources provide a unique opportunity to predict human behavior using machine learning methods. This study applies machine learning to analyze textual data derived from diverse sources: demographic data, scientific articles, employee documents, and social media content. The primary goal is to identify a person's area of interest and predict their behavior. We propose using Support Vector Machines (SVM) as a robust and versatile machine learning algorithm for text data analysis. SVM's ability to handle diverse data types makes it well-suited for analyzing complex human behavior patterns. By classifying documents into relevant topics, SVM can help assess how employee behavior aligns with organizational goals and performance metrics. This research aims to contribute to human behavior analysis by demonstrating the effectiveness of machine learning techniques, particularly SVM, in extracting meaningful insights from textual data.
Cite This Paper
Irada Alakbarova, "Analysis of Human Behavior and Interests Based on Text Data", International Journal of Education and Management Engineering (IJEME), Vol.15, No.1, pp. 1-9, 2025. DOI:10.5815/ijeme.2025.01.01
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