An In-Depth Analysis of Text Clustering Techniques for Identifying Potential Insurance Customers on Social Media: A Machine Learning Perspective

Abstract

Social media has emerged as a transformative platform for the exchange and dissemination of information. Unlike conventional sources such as online news, social media often offers more real-time and current updates. Effectively harnessing the vast and diverse pool of unstructured data on these platforms requires the extraction of structured information. This research focuses on the development of a social media web crawler, coupled with the implementation of sophisticated algorithms like Web Content Mining, Noisy Text Filtering, Named Entity Extraction, Part-Of-Speech (POS) Tagging, and Text Clustering. The aggregated information will be utilized to train a machine learning model capable of discerning a customer's preferred insurance type—be it accident, health, car, or life insurance. The overarching objective is to provide insurance companies with a swift, precise, and cost-effective means of identifying potential customers within the realm of social media. The result shows that this new technique has successfully identify relevant topic based on the comments and recommend corresponding insurance to the user