Industrial Memories: Exploring the Findings of Government Inquiries with Neural Word Embedding and Machine Learning

TL;DR

This paper introduces a text mining platform that uses neural word embeddings and machine learning to analyze government inquiry reports, making key findings more accessible and uncovering new historical insights.

Contribution

It presents a novel interactive system combining word embedding, classification, and visualization to explore large inquiry texts, enhancing accessibility and analysis.

Findings

Uncovered new historical insights from inquiry reports

Developed an interactive web-based exploration platform

Demonstrated effectiveness of neural embeddings in text analysis

Abstract

We present a text mining system to support the exploration of large volumes of text detailing the findings of government inquiries. Despite their historical significance and potential societal impact, key findings of inquiries are often hidden within lengthy documents and remain inaccessible to the general public. We transform the findings of the Irish government's inquiry into industrial schools and through the use of word embedding, text classification and visualisation, present an interactive web-based platform that enables the exploration of the text to uncover new historical insights.