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In this paper we describe our attempt at producing a state-of-the-art Twittersentiment classifier using Convolutional Neural Networks (CNNs) and Long ShortTerm Memory (LSTMs) networks. Our system leverages a large amount of unlabeleddata to pre-train word embeddings. We then use a subset of the unlabeled datato fine tune the embeddings using distant supervision. The final CNNs and LSTMsare trained on the SemEval-2017 Twitter dataset where the embeddings are finedtuned again. To boost performances we ensemble several CNNs and LSTMs together.Our approach achieved first rank on all of the five English subtasks amongst 40teams.

BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs