Critique: Sentiment-topic dynamic collaborative analysis-based public opinion mapping in aviation disaster management: A case study of the MU5735 air crash

In this study, we provide an in-depth evaluation of employing Latent Dirichlet Allocation (LDA) for topic modeling to understand social media textual data related to disaster risk management. By analyzing the architecture of LDA and its mechanics, we have outlined in the manuscript the problems of employing probabilistic models and bag-of-words architecture. We have analyzed how LDA struggles to capture the context and nuanced meanings within text. The LDA algorithm simplifies text analysis by treating each word equally, disregarding how words work together in phrases and sentences. This often leads to a mismatch between the identified topics and the true themes present in the text, which is particularly troubling when analyzing detailed reports or social media content about disasters where context is key.