  We describe the systems developed for the WNUT-2020 shared task 2,
identification of informative COVID-19 English Tweets. BERT is a highly
performant model for Natural Language Processing tasks. We increased BERT's
performance in this classification task by fine-tuning BERT and concatenating
its embeddings with Tweet-specific features and training a Support Vector
Machine (SVM) for classification (henceforth called BERT+). We compared its
performance to a suite of machine learning models. We used a Twitter specific
data cleaning pipeline and word-level TF-IDF to extract features for the
non-BERT models. BERT+ was the top performing model with an F1-score of 0.8713.
