  This paper describes the Duluth UROP systems that participated in
SemEval--2018 Task 2, Multilingual Emoji Prediction. We relied on a variety of
ensembles made up of classifiers using Naive Bayes, Logistic Regression, and
Random Forests. We used unigram and bigram features and tried to offset the
skewness of the data through the use of oversampling. Our task evaluation
results place us 19th of 48 systems in the English evaluation, and 5th of 21 in
the Spanish. After the evaluation we realized that some simple changes to
preprocessing could significantly improve our results. After making these
changes we attained results that would have placed us sixth in the English
evaluation, and second in the Spanish.
