Sentiment Analysis in Understanding the Potential of Online News in the Public Health Crisis Response

Abstract

This study analyzes online news disseminated throughout the pre-, during-, and post-intervention periods of the “Syphilis No!” Project, which was developed in Brazil between November 2018 and March 2019. We investigated the influence of sentiment aspects of news to explore their possible relationships with syphilis testing data in response to the syphilis epidemic in Brazil. A dictionary-based technique (VADER) was chosen to perform sentiment analysis considering the Brazilian Portuguese language. Finally, the data collected were used in statistical tests to obtain other indicators, such as correlation and distribution analysis. Of the 627 news items, 198 (31.58%) were classified as a sentiment of security (TP2; stands for the news type 2), whereas 429 (68.42%) were classified as sentiments that instilled vulnerability (TP3; stands for the news type 3). The correlation between the number of syphilis tests and the number of news types TP2 and TP3 was verified from (i) 2015 to 2017 and (ii) 2018 to 2019. For the TP2 type news, in all periods, the p-values were greater than 0.05, thus generating inconclusive results. From 2015 to 2017, there was an ⍴ = 0.33 correlation between TP3 news and testing data (p-value = 0.04); the years 2018 and 2019 presented a ⍴ = 0.67 correlation between TP3 news and the number of syphilis tests performed per month, with p-value = 0.0003. In addition, Granger’s test was performed between TP3 news and syphilis testing, which resulted in a p-value = 0.002, thus indicating the existence of Granger causality between these time series. By applying natural language processing to sentiment and informational content analysis of public health campaigns, it was found that the most substantial increase in testing was strongly related to attitude-inducing content (TP3).

Text mining analysis to understand the impact of online news on public health response: case of syphilis epidemic in Brazil

“…Notwithstanding limitation, the study reveals the effective application of a digital health system that incorporates all the elements of a complex public health campaign that included a communication campaign, education, health system interventions, training, expanded access to testing and treatment. The ability to explore online news through machine learning has aroused the interest of parallel study groups, bringing new insights for stakeholders to analyze public health campaigns from different perspectives, such as sentiment analysis techniques (35) and similarity analysis (36).…”