Thai Text Clustering with K-Means and TF-IDF in Python for Educational Applications
Keywords:
Thai Text Clustering, Qualitative Data, TF-IDF, K-Means, Python, Text MiningAbstract
Qualitative text data—such as student feedback, interview transcripts, and online content—are increasingly available from web-based sources and institutional repositories. However, their unstructured nature makes large-scale analysis difficult. Text clustering helps organize such data by grouping documents with similar content. This tutorial presents a step-by-step workflow for clustering Thai-language texts using TF-IDF and the K-means algorithm in Python. It covers preprocessing, vectorization, clustering, and evaluation, with code examples based on Thai-language documents. The tutorial concludes with examples of educational applications, including analyzing open-ended survey responses, exploring curriculum topics, and identifying emerging themes in academic writing.
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