A text data mining approach to the study of emotions triggered by new advertising formats during the COVID-19 pandemic

Under the influence of the health emergency triggered by the COVID-19 pandemic, many brands changed their communication strategy and included more or less explicit references to the principles of solidarity and fraternity in their TV commercials to boost the confidence and hope of Italian families during the lockdown. The traditional attitudes of the advertising format, which focused on product characteristics, were relegated to the background in order to reinforce the “brand image” through words, signs, hashtags and music that spread empathetic messages to all those who needed to regain hope and trust in a time of extreme emotional fragility. The objective of this paper is to identify the emotions and brand awareness during the lockdown using text mining techniques by measuring customer sentiment expressed on the Twitter social network. Our proposal starts from an unstructured corpus of 20,982 tweets processed with text data mining techniques to identify patterns and trends in people’s posts related to specific hashtags and TV ads produced during the COVID-19 pandemic. The innovations in the brand’s advertising among consumers seem to have triggered some sense of appreciation and gratitude, as well as a strong sense of belonging that was not present before, as the TV ads were perceived as a disruptive element in consumers’ tweets. Although this effect is clearly documented, in this paper we demonstrate its transitory nature, in the sense that the frequency of occurrence of terms associated with an emotional dimension peaks during the weeks of lockdown, and then gradually decreases.