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

EMNLP 2018 · Martin Schmitt, Simon Steinheber, Konrad Schreiber, Benjamin Roth ·

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.