"Variational Inference and Deep Learning: A New Synthesis", written by yours truly, is now available for D/L here:… https://t.co/zTb30bNM7t

In the work presented in this paper, we conduct experiments on sentiment analysis in Twitter messages by using a deep convolutional neural network. The network is trained on top of pre-trained word embeddings obtained by unsupervised learning on large text corpora. We use CNN with multiple filters with varying window sizes on top of which we add 2 fully connected layers with dropout and a softmax
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pages 464–469, Denver, Colorado, June 4-5, 2015. c 2015 Association for Computational Linguistics UNITN: Training Deep Convolutional Neural Network for Twitter Sentiment Classification Aliaksei Severyn DISI, University of Trento 38123 Povo (TN), Italy severyn@disi.unitn.it Alessandro Moschitti Qatar Computing Rese
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