Beyond Word-level to Sentence-level Sentiment Analysis for Financial Reports

摘要

This paper attempts to conduct a sentence-level sentiment analysis with respect to financial risk on a collection of financial reports. Specifically, we first propose a simple yet efficient algorithm to generate financial sentiment phrases (senti-phrases), and then with the obtained senti-phrases, we utilize multiple sentence embedding models for better learning the representations of financial risk sentences. In order to verify the performance of the proposed approach, we conduct a risk classification task of financial sentences on a sentence-level labeled dataset of finance reports. Experimental results show that incorporating the obtained senti-phrases into the embedding-based models improves the classification performance.