  In this work, we propose a new model for aspect-based sentiment analysis. In
contrast to previous approaches, we jointly model the detection of aspects and
the classification of their polarity in an end-to-end trainable neural network.
We conduct experiments with different neural architectures and word
representations on the recent GermEval 2017 dataset. We were able to show
considerable performance gains by using the joint modeling approach in all
settings compared to pipeline approaches. The combination of a convolutional
neural network and fasttext embeddings outperformed the best submission of the
shared task in 2017, establishing a new state of the art.
