Sentence-Level Sentiment Analysis of Financial News Using Distributed Text Representations and Multi-Instance Learning
This work addresses the need for investors and financial professionals to assess sentiment at the sentence-level in financial news, though it is incremental as it builds on existing methods for a specific domain.
The paper tackled the problem of fine-grained sentiment analysis in financial news by proposing a method using distributed text representations and multi-instance learning to transfer information from document-level to sentence-level, achieving a predictive accuracy of 69.90%, which exceeds alternative approaches by at least 3.80 percentage points.
Researchers and financial professionals require robust computerized tools that allow users to rapidly operationalize and assess the semantic textual content in financial news. However, existing methods commonly work at the document-level while deeper insights into the actual structure and the sentiment of individual sentences remain blurred. As a result, investors are required to apply the utmost attention and detailed, domain-specific knowledge in order to assess the information on a fine-grained basis. To facilitate this manual process, this paper proposes the use of distributed text representations and multi-instance learning to transfer information from the document-level to the sentence-level. Compared to alternative approaches, this method features superior predictive performance while preserving context and interpretability. Our analysis of a manually-labeled dataset yields a predictive accuracy of up to 69.90%, exceeding the performance of alternative approaches by at least 3.80 percentage points. Accordingly, this study not only benefits investors with regard to their financial decision-making, but also helps companies to communicate their messages as intended.