FINKI at semeval-2016 task 4: Deep learning architecture for twitter sentiment analysis

Abstract

In this paper, we present a novel deep learning architecture for sentiment analysis in Twitter messages. Our system finki, employs both convolutional and gated recurrent neural networks to obtain a more diverse tweet representation. The network is trained on top of GloVe word embeddings pre-trained on the Common Crawl dataset. Both neural networks are used to obtain a fixed length representation of variable sized tweets, and the concatenation of these vectors is supplied to a fully connected softmax layer with dropout regularization. The system is evaluated on benchmark datasets from the Sentiment Analysis in Twitter task of the SemEval 2016 challenge where our model achieves best and second highest results on the 2-point and 5-point quantification subtasks respectively. Despite not relying on any hand-crafted features, our system manages the second highest average rank on the considered subtasks.