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The topic of aspect-based sentiment analysis (ABSA) has been explored for avariety of industries, but it still remains much unexplored in finance. Therecent release of data for an open challenge (FiQA) from the companionproceedings of WWW '18 has provided valuable finance-specific annotations. FiQAcontains high quality labels, but it still lacks data quantity to applytraditional ABSA deep learning architecture. In this paper, we employhigh-level semantic representations and methods of inductive transfer learningfor NLP. We experiment with extensions of recently developed domain adaptationmethods and target task fine-tuning that significantly improve performance on asmall dataset. Our results show an 8.7% improvement in the F1 score forclassification and an 11% improvement over the MSE for regression on currentstate-of-the-art results.

Financial Aspect-Based Sentiment Analysis using Deep Representations