Analyzing public sentiments on the Cullen Commission inquiry into money laundering: harnessing deep learning in the AI of Things Era

Abstract

This study employs deep learning methodologies to conduct sentiment analysis of tweets related to the Cullen Commission’s inquiry into money laundering in British Columbia. The investigation utilizes CNN, RNN + LSTM, GloVe, and BERT algorithms to analyze sentiment and predict sentiment classes in public reactions when the Commission was announced and after the final report’s release. Results reveal that the emotional class “joy” predominated initially, reflecting a positive response to the inquiry, while “sadness” and “anger” dominated after the report, indicating public dissatisfaction with the findings. The algorithms consistently predicted negative, neutral, and positive sentiments, with BERT showing exceptional precision, recall, and F1-scores. However, GloVe displayed weaker and less consistent performance. Criticisms of the Commission’s efforts relate to its inability to expose the full extent of money laundering, potentially influenced by biased testimonies and a narrow investigation scope. The public’s sentiments highlight the awareness raised by the Commission and underscore the importance of its recommendations in combating money laundering. Future research should consider broader stakeholder perspectives and objective assessments of the findings.