The analysis of news in the financial context has gained a prominent interest in the last years. This because of the possible predictive power of such content especially in terms of associated sentiment/mood. In this paper we focus on a specific aspect of financial news analysis: how the covered topics modify according to space and time dimensions. To this purpose, we employ a modified version of the well known LDA model that takes into account covariates as well. Our results show that both the temporal dynamic and the differentiation with regards to considered countries matter in explaining differences in the results.