In this paper we describe a deep learning system that has been built for SemEval 2016 Task4 (Subtask A and B). In this work we trained a Gated Recurrent Unit (GRU) neural network model on top of two sets of word embeddings: (a) general word embeddings generated from unsupervised neural language model; and (b) task specific word embeddings generated from supervised neural language model that was trained to classify tweets into positive and negative categories. We also added a method for analyzing and splitting multi-words hashtags and appending them to the tweet body before feeding it to our model. Our models achieved 0.58 F1-measure for Subtask A (ranked 12/34) and 0.679 Recall for Subtask B (ranked 12/19).
|Original language||English (US)|
|Title of host publication||Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)|
|Publisher||Association for Computational Linguistics (ACL)|
|State||Published - Jul 14 2016|