In this paper, we propose a deep learning system for classification of tweets on a two-point scale. Our architecture consists of a multilayered recurrent neural network having gated recurrent units. The network is pre-trained with a weakly labeled dataset of tweets to learn the sentiment specific embeddings. Then it is fine tuned on the given training dataset of the task 4B in SemEval-2016. The network does very little pre-processing for raw tweets and no post-processing at all. The proposed system achieves 3rd rank on the leaderboard of task 4B.
CITATION STYLE
Yadav, V. (2016). Thecerealkiller at SemEval-2016 task 4: Deep learning based system for classifying sentiment of tweets on two point scale. In SemEval 2016 - 10th International Workshop on Semantic Evaluation, Proceedings (pp. 100–102). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/s16-1013
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