用LDA、NMF和PLSA分析航空安全叙事：基于Socrata数据集的案例研究
Analyzing Aviation Safety Narratives with LDA, NMF and PLSA: A Case Study Using Socrata Datasets
AI总结：
本文在1908-2009年Socrata航空安全数据上比较LDA、NMF和PLSA三种主题建模方法，发现LDA一致性最高但困惑度也最高，证明主题建模可有效提取航空安全风险因素和改进方向。
摘要：
本研究探讨了主题建模技术——潜在狄利克雷分配（LDA）、非负矩阵分解（NMF）和概率潜在语义分析（PLSA）——在1908年至2009年Socrata数据集上的应用。按运营者类型（军用、商用和私人）分类后，分析识别出飞行员失误、机械故障、天气条件和训练不足等关键主题。研究突出了每种方法的独特优势：LDA能够发现重叠主题，NMF能够生成清晰且可解释的主题，PLSA尽管解释上较为复杂，但能提供细致的概率洞察。统计分析显示，PLSA的一致性得分为0.32，困惑度值为-4.6；NMF得分为0.34和37.1；而LDA取得了最高的一致性得分0.36，但困惑度也最高，为38.2。这些发现证明了主题建模在从非结构化航空安全叙事中提取可操作见解方面的价值，有助于识别风险因素和各领域的改进方向。未来方向包括整合额外的上下文变量、利用神经主题模型以及加强航空安全协议。本研究为航空安全管理中的高级文本挖掘应用奠定了基础。
英文摘要：
This study explores the application of topic modelling techniques Latent Dirichlet Allocation (LDA), Nonnegative Matrix Factorization (NMF), and Probabilistic Latent Semantic Analysis (PLSA) on the Socrata dataset spanning from 1908 to 2009. Categorized by operator type (military, commercial, and private), the analysis identified key themes such as pilot error, mechanical failure, weather conditions, and training deficiencies. The study highlights the unique strengths of each method: LDA ability to uncover overlapping themes, NMF production of distinct and interpretable topics, and PLSA nuanced probabilistic insights despite interpretative complexity. Statistical analysis revealed that PLSA achieved a coherence score of 0.32 and a perplexity value of -4.6, NMF scored 0.34 and 37.1, while LDA achieved the highest coherence of 0.36 but recorded the highest perplexity at 38.2. These findings demonstrate the value of topic modelling in extracting actionable insights from unstructured aviation safety narratives, aiding in the identification of risk factors and areas for improvement across sectors. Future directions include integrating additional contextual variables, leveraging neural topic models, and enhancing aviation safety protocols. This research provides a foundation for advanced text-mining applications in aviation safety management.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
