text data

Natural Language Processing (NLP) can automate the process of topic modeling in text data by using advanced algorithms to analyze and extract patterns in the text. NLP techniques such as tokenization, stemming, and lemmatization help in preprocessing the text data, while topic modeling algorithms like Latent Dirichlet Allocation (LDA) and Non-Negative Matrix Factorization (NMF) can uncover themes and topics from the text. By combining NLP and topic modeling, businesses can streamline the process of extracting meaningful insights from large volumes of text data efficiently.