Yanxi Liu
2022
UOA at the FinNLP-2022 ERAI Task: Leveraging the Class Label Description for Financial Opinion Mining
Jinan Zou
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Haiyao Cao
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Yanxi Liu
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Lingqiao Liu
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Ehsan Abbasnejad
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Javen Qinfeng Shi
Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP)
Evaluating the Rationales of Amateur Investors (ERAI) is a task about mining expert-like viewpoints from social media. This paper summarizes our solutions to the ERAI shared task, which is co-located with the FinNLP workshop at EMNLP 2022. There are 2 sub-tasks in ERAI. Sub-task 1 is a pair-wised comparison task, where we propose a BERT-based pre-trained model projecting opinion pairs in a common space for classification. Sub-task 2 is an unsupervised learning task ranking the opinions’ maximal potential profit (MPP) and maximal loss (ML), where our model leverages the regression method and multi-layer perceptron to rank the MPP and ML values. The proposed approaches achieve competitive accuracy of 54.02% on ML Accuracy and 51.72% on MPP Accuracy for pairwise tasks, also 12.35% and -9.39% regression unsupervised ranking task for MPP and ML.
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