  The Web has become the main platform where people express their opinions
about entities of interest and their associated aspects. Aspect-Based Sentiment
Analysis (ABSA) aims to automatically compute the sentiment towards these
aspects from opinionated text. In this paper we extend the state-of-the-art
Hybrid Approach for Aspect-Based Sentiment Analysis (HAABSA) method in two
directions. First we replace the non-contextual word embeddings with deep
contextual word embeddings in order to better cope with the word semantics in a
given text. Second, we use hierarchical attention by adding an extra attention
layer to the HAABSA high-level representations in order to increase the method
flexibility in modeling the input data. Using two standard datasets (SemEval
2015 and SemEval 2016) we show that the proposed extensions improve the
accuracy of the built model for ABSA.
