Sentiment analysis and causal learning of COVID-19 tweets prior to the rollout of vaccines

Abstract

While the impact of the COVID-19 pandemic has been widely studied, relatively fewer discussions about the sentimental reaction of the public are available. In this article, we scrape COVID-19 related tweets on the microblogging platform, Twitter, and examine the tweets from February 24, 2020 to October 14, 2020 in four Canadian cities (Toronto, Montreal, Vancouver, and Calgary) and four U.S. cities (New York, Los Angeles, Chicago, and Seattle). Applying the RoBERTa, Vader and NRC approaches, we evaluate sentiment intensity scores and visualize the results over different periods of the pandemic. Sentiment scores for the tweets concerning three anti-epidemic measures, “masks”, “vaccine”, and “lockdown”, are computed for comparison. We explore possible causal relationships among the variables concerning tweet activities and sentiment scores of COVID-19 related tweets by integrating the echo state network method with convergent cross-mapping. Our analyses show that public sentiments about COVID-19 vary from time to time and from place to place, and are different with respect to anti-epidemic measures of “masks”, “vaccines”, and “lockdown”. Evidence of the causal relationship is revealed for the examined variables, assuming the suggested model is feasible.

Metode SVM dan Naive Bayes untuk Analisis Sentimen ChatGPT di Twitter

“…Serta model Naïve Bayes dalam analisis sentimen [7]. Sementara itu, studi lain telah membandingkan efektivitas model sentimen seperti Vader dan RoBERTa [8], [9]. Namun belum ada penelitian yang menggabungkan semua faktor tersebut dalam konteks reaksi pengguna Twitter terhadap ChatGPT.…”

A Novel Approach for the Early Detection of Medical Resource Demand Surges During Health Care Emergencies: Infodemiology Study of Tweets

“…Furthermore, forecasting models have been created to track demand for ICU capacity planning in countries such as Chile [ 32 , 33 ], Brazil [ 34 ], Colombia [ 35 ], the United States [ 36 ], India [ 37 ], and China [ 38 ]. Previous studies have applied convergent cross-mapping (CCM) analysis to explore possible relationships involving antiepidemic measure–related tweets [ 39 ], the dynamics of misleading news on Twitter [ 40 ], and the identification of the global drivers of influenza [ 41 ]. However, to our knowledge, there are limited studies examining the potential of social media, particularly Twitter, to better understand hospital bed demand.…”

Exploring public sentiment and vaccination uptake of COVID-19 vaccines in England: a spatiotemporal and sociodemographic analysis of Twitter data

“…During the COVID-19 pandemic, social media platforms played a crucial role in understanding public sentiment towards the COVID-19 vaccines. Various studies worldwide were conducted to analyse the data and identify public concerns, misinformation, and acceptance of the vaccines (16)(17)(18)(19). These examples imply that social media can be a valuable resource for evaluating vaccination intention and identifying potential obstacles to vaccine uptake since it offers a platform for individuals to voice their views and opinions publicly.…”