The shift from traditional bag-of-words models discussed in Chapters 11 and 12 to advanced techniques such as word embeddings, contextual embeddings, and attention mechanisms has significantly enhanced the ability of algorithms to capture semantic nuances and contextual meaning in texts. This chapter explores text analysis advancements enabled by Large Language Models (LLMs) like Google’s BERT, OpenAI’s GPT, and Meta’s BART, highlighting their emerging properties like transfer learning, zero-shot, and few-shot learning, which extended the utility of LLMs, making them suitable for text analysis tasks such as sentiment analysis, classification, summarization, and topic modeling. These practical applications are illustrated with discussions on using BERT and ChatGPT for topic modeling and sentiment analysis. The Python lab demonstrates how to apply this knowledge to practical text analysis: sentiment analysis with BERT, document classification with BART, and topic modeling with BERTopic LLM.