Analyzing public sentiments on the Cullen Commission inquiry into money laundering: harnessing deep learning in the AI of Things Era
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".