👉 Latent Mathematics (LatMath) is a framework that uses Latent Dirichlet Allocation (LDA), a probabilistic topic modeling technique, to uncover hidden thematic structures within large text corpora. In essence, LatMath treats documents as mixtures of topics and topics as distributions over words. Each document is represented as a distribution over topics, and each topic as a distribution over words, capturing the probability of each word appearing in that topic. By applying LDA, LatMath can identify latent topics that are not explicitly stated but are inferred from the co-occurrence patterns of words across documents. This method allows for a nuanced understanding of textual data, enabling applications like document classification, information retrieval, and trend analysis in various fields such as social sciences, business, and media studies.
