This project aims to extract the social trends from the news article automatically. By analysing the content of articles, it captures real-time insights into consumer behavior and optimal marketing opportunities. Unlike traditional manual research, this fully automated pipeline allows businesses to pivot their strategies instantly, maintaining a competitive edge in a shifting landscape.

Using the Brisbane Tourism industry as a case study, the project transforms unstructured article text into numerical data via TF-IDF Vectorization. It then executes K-Means Clustering Unsupervised Machine Learning to group articles by themes and ensuring the resulting market insights are derived through statistical rigor.