  We propose a novel two-layered attention network based on Bidirectional Long
Short-Term Memory for sentiment analysis. The novel two-layered attention
network takes advantage of the external knowledge bases to improve the
sentiment prediction. It uses the Knowledge Graph Embedding generated using the
WordNet. We build our model by combining the two-layered attention network with
the supervised model based on Support Vector Regression using a Multilayer
Perceptron network for sentiment analysis. We evaluate our model on the
benchmark dataset of SemEval 2017 Task 5. Experimental results show that the
proposed model surpasses the top system of SemEval 2017 Task 5. The model
performs significantly better by improving the state-of-the-art system at
SemEval 2017 Task 5 by 1.7 and 3.7 points for sub-tracks 1 and 2 respectively.
