ABSTRACT
Since 2016, Istat has published the Social Mood on Economy Index (SMEI), an experimental high-frequency sentiment index derived from public tweets in Italian. Since the economic shock produced by the Covid-19 pandemic has not significantly affected the SMEI series, we wondered to what extent the SMEI could grasp the change in the mood due to the pandemic. We produced alternative sentiment indicators, and we compared them to nontraditional high-frequency series to assess the coherence of the SMEI. An alternative index, calculated by introducing pandemic-related terms in the lexicon used for sentiment analysis, better grasped the negative economic trend during the pandemic. We concluded that a continuous adaptation of the dictionary in lexicon-based techniques could improve the coherence. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.