CADEN: A Context-Aware Deep Embedding Network for Financial Opinions Mining

Following the recent advances of artificial intelligence, financial text mining has gained new potential to benefit theoretical research with practice impacts. An essential research question for financial text mining is how to accurately identify the actual financial opinions (e.g., bullish or bearish) behind words in plain text. Traditional methods mainly consider this task as a text classification problem with solutions based on machine learning algorithms. However, most of them rely heavily on the hand-crafted features extracted from the text. Indeed, a critical issue along this line is that the latent global and local contexts of the financial opinions usually cannot be fully captured. To this end, we propose a context-aware deep embedding network for financial text mining, named CADEN, by jointly encoding the global and local contextual information. Especially, we capture and include an attitude-aware user embedding to enhance the performance of our model. We validate our method with extensive experiments based on a real-world dataset and several state-of-the-art baselines for investor sentiment recognition. Our results show a consistently superior performance of our approach for identifying the financial opinions from texts of different formats.