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.
Authors’ contributions—DZ was a major contributor in writing para 3.1, EC in writing para 4.1 and LV in writing para 4.2. AR was a major contributor in writing paragraphs 1, 2, 3.2 and 3.3.
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Notes
- 1.
The transformation makes equal to zero the SMEI long-run mean (referred to the baseline period 10 February 2016–30 September 2018).
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Righi, A., Catanese, E., Valentino, L., Zardetto, D. (2022). The Italian Social Mood on Economy Index During the Covid-19 Crisis. In: Salvati, N., Perna, C., Marchetti, S., Chambers, R. (eds) Studies in Theoretical and Applied Statistics . SIS 2021. Springer Proceedings in Mathematics & Statistics, vol 406. Springer, Cham. https://doi.org/10.1007/978-3-031-16609-9_29
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