A Framework for Facilitating Reproducible News Sentiment Impact Analysis

Abstract

The proliferation of outlets for news media in recent decades has contributed to faster issuance of news data. News analysis has been one of the key activities conducted by researchers in a broad variety of research disciplines. In general, the analysis process used in these studies includes interpreting the content of the news items, and then discovering their impact in a specific area. In this paper, we delve into the field of news analysis applied to the financial domain and explore news sentiment impact analysis in the context of financial markets. Existing studies lack systematic methods to assimilate financial context and evaluate the impact of a given news dataset on relevant entities financial market performance. We introduce an improved version of the framework called News Sentiment Impact Analysis (NSIA) that encompasses models, supporting software architecture and processes for defining various financial contexts and conducting news sentiment impact analysis. The framework is then evaluated using a prototype implementation and a case study that investigates the impact of extremely negative news on the stock price of the related entities. The results demonstrate the functionality, usability and reproducibility of the framework, and its capability to bridge the gap between generating news sentiment and evaluating its impact in selected financial contexts.