Comparing BERTopic with LDA
LDA is a classical probabilistic model for topic modeling, whereas BERTopic leverages transformer-based models to create more context-aware and semantically meaningful topic representations. They come from two different literatures, each with its own set of characteristics and applications. The choice between the two depends on the specific needs of your NLP task and the nature of your text data. Here are the key differences between LDA and BERTopic.
Approach
LDA is a generative probabilistic model for topic modeling. It assumes that documents are mixtures of topics, and topics are mixtures of words. LDA aims to discover these underlying topics and the distribution of words within them.
BERTopic, on the other hand, uses transformer-based language models, such as BERT, to generate document embeddings. It then incorporates UMAP for dimensionality reduction, DBSCAN for initial clustering, c-TFIDF to highlight significant terms, and MMR for keyword...
