News analysis activities have been the focus of many research studies across various life domains. So often, the goal of these studies is to automatically, analyze the meaning of news, and to gauge their impact on a particular domain. In this paper, we focus on studying sentiment analysis impact, on financial markets. Current studies, lack systematic approaches to evaluate the impact of a given sentiment dataset, in different financial contexts. We introduce a framework that encompasses models, processes, and a supporting software architecture for defining different financial contexts and conducting sentiment data-sets evaluation. The paper, describes a prototype implementation of the framework and a case study, which investigates the efficacy of the framework in evaluating the impact of a particular news sentiment dataset. The results demonstrate the capability of the framework in bridging the gap between producing a sentiment dataset and evaluating its impact in various financial contexts.
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