Joint Aspect and Polarity Classification for Aspect-based Sentiment Analysis with End-to-End Neural Networks
This work addresses sentiment analysis for text data by improving accuracy over pipeline methods, though it is incremental as it builds on existing neural approaches.
The authors tackled aspect-based sentiment analysis by jointly modeling aspect detection and polarity classification in an end-to-end neural network, achieving a new state of the art on the GermEval 2017 dataset with a combination of CNN and fasttext embeddings.
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.