Topic modeling using big data analytics can uncover hidden patterns in large document collections. It involves preprocessing large datasets, running modeling algorithms like LDA and PLSI in parallel across multiple nodes of a Hadoop cluster. This significantly improves computation time over a single machine. Topic modeling tools like Mallet and LDA R packages are used to automatically discover topics in text corpora. Applications include analyzing news articles, improving search engine rankings, and finding patterns in genetic and image data.