Duluth UROP at SemEval-2018 Task 2: Multilingual Emoji Prediction with Ensemble Learning and Oversampling
This work addresses emoji prediction for multilingual text processing, but it is incremental as it applies existing methods to a new dataset.
The paper tackled multilingual emoji prediction by using ensemble learning and oversampling, achieving 19th out of 48 in English and 5th out of 21 in Spanish initially, with post-evaluation improvements potentially raising rankings to 6th in English and 2nd in Spanish.
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.