Fine-grained analysis of explicit and implicit sentiment in financial news articles

In the financial domain, news has an impact on the stock markets.Most sentiment analysis methods are coarse-grained and focus on explicit sentiment.Such a method is insufficient to detect topic-specific sentiment in financial news articles.We propose a novel fine-grained method that detects explicit and implicit sentiment.This is a viable method for topic-specific sentiment analysis in financial news text. This paper focuses on topic-specific and more specifically company-specific sentiment analysis in financial newswire text. This application is of great use to researchers in the financial domain who study the impact of news (media) on the stock markets.We investigate the viability of a new fine-grained sentiment annotation scheme. Most of the current approaches to sentiment analysis focus on the detection of explicit sentiment. As news text often contains implicit sentiment, i.e. factual information implying positive or negative sentiment, our approach aims to identify both explicit and implicit sentiment. Furthermore, this sentiment is analyzed on a fine-grained level by detecting the topic of the sentiment, as sentiment is not always expressed towards the topics one is interested in.In order to test our approach, we assembled a corpus of company-specific news articles, which was manually labeled by four annotators to create a gold standard. We compare the results of our method to the performance of two coarse-grained baseline systems: a lexicon-based approach and a supervised machine learning approach that makes use of lexical features. Our fine-grained approach outperforms both baselines, and its output shows substantial to almost perfect agreement with the gold standard sentiment labels. Using our annotation scheme, we are able to filter out irrelevant sentiment expressions and detect explicit and implicit sentiment in a reliable way.