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

Joint Aspect and Polarity Classification for Aspect-based Sentiment Analysis with End-to-End Neural Networks