UIUC at SemEval-2018 Task 1: Recognizing Affect with Ensemble Models

Our submission to the SemEval-2018 Task1: Affect in Tweets shared task competition is a supervised learning model relying on standard lexicon features coupled with word embedding features. We used an ensemble of diverse models, including random forests, gradient boosted trees, and linear models, corrected for training-development set mismatch. We submitted the system's output for subtasks 1 (emotion intensity prediction), 2 (emotion ordinal classification), 3 (valence intensity regression) and 4 (valence ordinal classification), for English tweets. We placed 25^th, 19^th, 24^th and 15^th in the four subtasks respectively. The baseline considered was an SVM (Support Vector Machines) model with linear kernel on the lexicon and embedding based features. Our system's final performance measured in Pearson correlation scores outperformed the baseline by a margin of 2.2% to 14.6% across all tasks.