  Every year, thousands of people receive consumer product related injuries.
Research indicates that online customer reviews can be processed to
autonomously identify product safety issues. Early identification of safety
issues can lead to earlier recalls, and thus fewer injuries and deaths. A
dataset of product reviews from Amazon.com was compiled, along with
\emph{SaferProducts.gov} complaints and recall descriptions from the Consumer
Product Safety Commission (CPSC) and European Commission Rapid Alert system. A
system was built to clean the collected text and to extract relevant features.
Dimensionality reduction was performed by computing feature relevance through a
Random Forest and discarding features with low information gain. Various
classifiers were analyzed, including Logistic Regression, SVMs,
Na{\"i}ve-Bayes, Random Forests, and an Ensemble classifier. Experimentation
with various features and classifier combinations resulted in a logistic
regression model with 66\% precision in the top 50 reviews surfaced. This
classifier outperforms all benchmarks set by related literature and consumer
product safety professionals.
