This story was originally published on HackerNoon at: https://hackernoon.com/effective-anomaly-detection-pipeline-for-amazon-reviews-references-and-appendix. Explore findings from a study on an anomaly detection pipeline for Amazon reviews using MPNet embeddings. Check more stories related to machine-learning at: https://hackernoon.com/c/machine-learning. You can also check exclusive content about #transformers, #anomaly-detection, #nlp-for-anomaly-detection, #explainability-in-ml, #machine-learning-classifiers, #text-specific-ad-models, #text-encoding-techniques, #explainable-ai, and more. This story was written by: @textmodels. Learn more about this writer by checking @textmodels's about page, and for more stories, please visit hackernoon.com. This study introduces an effective pipeline for detecting anomalous Amazon reviews using MPNet embeddings. It evaluates SHAP, term frequency, and GPT-3 for explainability, revealing user preferences and computational challenges. Future research may explore broader surveys and integrating GPT-3 throughout the pipeline for enhanced performance.