View Proposal

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Proposer

Ian Tan

Title

A Framework for Automated Discovery, Multi-Taxonomic Classification, and Geospatial Mapping of Global Enterprise AI Use Cases

Goal

To design and implement an autonomous news scraper that aggregates global enterprise AI news content, classifies them using text analytics techniques, and visualise through a geospatial and infographics platform

Description

The proliferation of AI across various economic sectors has generated vast quantities of unstructured information detailing enterprise implementations, some pilot projects, and successful/unsuccessful operational outcomes. However, enterpreneurs, researchers, and policymakers face significant information asymmetry when trying to track where and how AI is actively deployed worldwide. This project proposes an automated, end-to-end system designed to continuously harvest, structure, and visualise global enterprise AI news, focused specifically on real-world business applications of AI. The framework integrates three core pillars: dynamic Web ingestion (think of this as an Agentic AI process rather than traditional web scraping), text content classification (data science), and geospatial visualisation (look at what the GDELT Project is about).

Resources

AI tokens (to be sponsored - this is an industry collaboration)

Background

Url

Difficulty Level

Moderate

Ethical Approval

None

Number Of Students

2

Supervisor

Ian Tan

Keywords

agentic ai, text classification, geospatial visualisation, ai news aggregation

Degrees

Bachelor of Science in Computing Science