Overseas Construction Suppliers Assessment based on Clustering and Sentiment Analysis

The Global construction projects requires a combination of contractors, suppliers, Engineers, Real estate people, Labors in foreign soil. It is better to know or assess the team before involving them with major projects or government-based projects. The article proposes an innovative approach to evaluate overseas construction suppliers using clustering and sentiment analysis techniques which analyze the textual data from various sources, such as customer reviews and supplier communications. The sentiment scores are utilized for supplier classification using machine learning algorithms. This work study and validates the effectiveness of our approach in assisting construction companies with supplier selection and risk management. Despite some limitations, our approach provides valuable insights into supplier performance and reputation. By leveraging sentiment analysis, construction companies can make informed decisions and enhance project outcomes through better supplier management. The method also involves K-Mean clustering to set up the construction unit to increase the score of the construction supply to the projects. This research contributes to advancing sentiment analysis applications in the construction industry on a global scale..