  Starting from a corpus of economic articles from The Wall Street Journal, we
present a novel systematic way to analyse news content that evolves over time.
We leverage on state-of-the-art natural language processing techniques (i.e.
GPT3.5) to extract the most important entities of each article available, and
aggregate co-occurrence of entities in a related graph at the weekly level.
Network analysis techniques and fuzzy community detection are tested on the
proposed set of graphs, and a framework is introduced that allows systematic
but interpretable detection of topics and narratives. In parallel, we propose
to consider the sentiment around main entities of an article as a more accurate
proxy for the overall sentiment of such piece of text, and describe a
case-study to motivate this choice. Finally, we design features that
characterise the type and structure of news within each week, and map them to
moments of financial markets dislocations. The latter are identified as dates
with unusually high volatility across asset classes, and we find quantitative
evidence that they relate to instances of high entropy in the high-dimensional
space of interconnected news. This result further motivates the pursued efforts
to provide a novel framework for the systematic analysis of narratives within
news.
