Sentiment and emotion in financial journalism: a corpus-based, cross-linguistic analysis of the effects of COVID

Abstract

Sentiment and emotion play a crucial role in financial journalism, influencing market perceptions and reactions. However, the impact of the COVID-19 crisis on the language used in financial newspapers remains underexplored. The present study addresses this gap by comparing data from specialized financial newspapers in English and Spanish, focusing on the years immediately prior to the COVID-19 crisis (2018–2019) and during the pandemic itself (2020–2021). We aim to explore how the economic upheaval of the latter period was conveyed in these publications and investigate the changes in sentiment and emotion in their language compared to the previous timeframe. To this end, we compiled comparable corpora of news items from two respected financial newspapers (The Economist and Expansión), covering both the pre-COVID and pandemic periods. Our corpus-based, contrastive EN-ES analysis of lexically polarized words and emotions allows us to describe the publications’ positioning in the two periods. We further filter lexical items using the CNN Business Fear and Greed Index, as fear and greed are the opposing emotional states most often linked to financial market unpredictability and volatility. This novel analysis is expected to provide a holistic picture of how these specialist periodicals in English and Spanish have emotionally verbalized the economic havoc of the COVID-19 period compared to their previous linguistic behaviour. By doing so, our study contributes to the understanding of sentiment and emotion in financial journalism, shedding light on how crises can reshape the linguistic landscape of the industry.