Progress in Computing Applications Maleerat Maliyaem 15 1 2026 https://doi.org/10.6025/pca/2026/15/1/18-29 https://www.dline.info/pca/fulltext/v15n1/pcav15n1_2.pdf This study demonstrates how unsupervised machine learning techniques can audit and optimize technical knowledge repositories through systematic semantic analysis of a curated Python FAQ corpus comprising 163 question answer pairs. Addressing the challenge that over 80% of organizational data exists in unstructured text form, we implement a transparent, eight stage natural language processing pipeline encompassing preprocessing, TF-IDF feature extraction, cosine similarity analysis, Latent Dirichlet Allocation (LDA) topic modeling, Principal Component Analysis (PCA) dimensionality reduction, and network-based co-occurrence analysis. Our methodology emphasizes contextualized preprocessing decisions, responding to contemporary gaps in methodological transparency within organizational text mining research. Results reveal five coherent thematic clusters corresponding to core Python programming concepts: language fundamentals, data structures, syntax and control flow, function operations, and advanced mechanisms. Cosine similarity analysis identifies non trivial semantic overlap among FAQ entries, highlighting actionable opportunities for content consolidation to reduce redundancy and improve retrieval efficiency. Network analysis establishes "python," "function," "list," and "dictionary" as high centrality conceptual anchors within the corpus topology. These findings translate into practical recommendations for technical documentation management, including evidence based FAQ merging, hierarchically organized navigation schemas, and gap identification for underrepresented subject areas. By bridging unstructured text data with structured organizational intelligence, this reproducible framework supports educational chatbot development, curriculum design, and knowledge base optimization. The study underscores that rigorous, contextually justified preprocessing combined with multi perspective unsupervised analytics enables researchers and practitioners to unlock significant value from complex text corpora while ensuring methodological transparency and analytical validity in organizational research applications.