TITLE:
A Sentiment Analysis Approach to Discover Public Panic: Based on Weibo Covid-19 Data
AUTHORS:
Wanjun Wu
KEYWORDS:
Sentiment Analysis, Data Analysis, Covid-19, Micro-Blogdata
JOURNAL NAME:
Social Networking,
Vol.11 No.3,
July
29,
2022
ABSTRACT: Background: Weibo
is a Twitter-like micro-blog platform in China where people post their
real-life events as well as express their feelings in short texts. Since the
outbreak of the Covid-19 pandemic, thousands of people have expressed their concerns and worries about the
outbreak via Weibo, showing the existence
of public panic. Methods: This paper comes up with a sentiment analysis approach to discover public panic. First, we used Octoparse to obtain
Weibo posts about the hot topic Covid-19 Pandemic. Second, we break down those
sentences into independent words and clean the data by removing stop words. Then, we use the sentiment score function
that deals with negative words, adverbs, and sentiment words to get the
sentiment score of each Weibo post. Results: We observe the distribution
of sentiment scores and get the benchmark to evaluate public panic. Also, we
apply the same process to test the mass sentiment under other topics to test
the efficiency of the sentiment function, which shows that our function works
well.