What method is used for discovering latent topics within a text and identifying relevant words associated with those topics?

Latent Dirichlet Allocation (LDA) is a powerful generative statistical model used to discover latent topics within a collection of documents. It allows for the identification of topics that may not be immediately visible and assigns a distribution of words to each topic. LDA works by assuming that documents are generated by a mixture of topics, where each topic is characterized by a distribution over words.

In using LDA, each document is represented as a mixture of these inferred topics, and the model reveals the underlying structure of the text data. As a result, LDA effectively identifies relevant words that are associated with each discovered topic, thereby giving insights into the thematic content of large sets of documents. This makes it particularly useful for exploratory text analysis and summarization.

The other methods mentioned serve different purposes. For example, word embeddings focus on representing words in continuous vector space to capture semantic meanings, while the vector space model is concerned with representing text documents as vectors in a multi-dimensional space for purposes like information retrieval. Term Frequency-Inverse Document Frequency (TF-IDF) is primarily a technique for weighting the importance of words in documents relative to their occurrence across a corpus, rather than identifying latent topics. Hence, LDA is the most suitable method for discovering latent