Mineração de dados em tweets para análise do consumo de lácteos no Brasil

Thallys da Silva Nogueira; Anna Letícia Franco Monteiro; Darlan Henrique da Costa Silva; Kennya Beatriz Siqueira; Priscila Vanessa Zabala Capriles Goliatt

doi:10.18540/jcecvl8iss10pp14863-01a

Authors

Thallys da Silva Nogueira Universidade Federal de Juiz de Fora, Brasil https://orcid.org/0000-0002-8499-0181
Anna Letícia Franco Monteiro Universidade Federal de Juiz de Fora, Brasil
Darlan Henrique da Costa Silva Universidade Federal de Juiz de Fora, Brasil
Kennya Beatriz Siqueira Empresa Brasileira de Pesquisa Agropecuária, Brasil
Priscila Vanessa Zabala Capriles Goliatt Universidade Federal de Juiz de Fora, Brasil

DOI:

https://doi.org/10.18540/jcecvl8iss10pp14863-01a

Keywords:

Consumer. Milk and derivatives. Artificial intelligence. Social networks. Market research.

Abstract

Brazilians' daily routine was affected by the COVID-19 epidemic in a number of ways, with food being one of them. This study used data from the social network Twitter and the tool Observatório do Consumidor to examine the consumption of dairy products in Brazil in recent years. Natural language processing techniques were applied to the data to determine the verbs relating to consumption and their respective frequencies over time in order to respond to the queries "Which are the most consumed dairy products in Brazil?" and "How was this consumption over time?". It was found that the five dairy products with the largest number of consumption-related verb references were ice cream, condensed milk, cheese, dulce de leche, and milk, making them the most popular choices during the study period. However, it was noted that since 2020, dairy consumption has been declining. These findings demonstrate that it is feasible to swiftly, dynamically, and affordably assess food consumption through social networks.