About: COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification
Creator	Rahman, Esawi, Ek Nawaz Ali, G Samuel, Jim Samuel, Yana
source	ArXiv
abstract	Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fueled by incomplete and often inaccurate information. There is therefore a tremendous need to address and better understand COVID-19's informational crisis and gauge public sentiment, so that appropriate messaging and policy decisions can be implemented. In this research article, we identify public sentiment associated with the pandemic using Coronavirus specific Tweets and R statistical software, along with its sentiment analysis packages. We demonstrate insights into the progress of fear-sentiment over time as COVID-19 approached peak levels in the United States, using descriptive textual analytics supported by necessary textual data visualizations. Furthermore, we provide a methodological overview of two essential machine learning (ML) classification methods, in the context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths. We observe a strong classification accuracy of 91% for short Tweets, with the Naive Bayes method. We also observe that the logistic regression classification method provides a reasonable accuracy of 74% with shorter Tweets, and both methods showed relatively weaker performance for longer Tweets. This research provides insights into Coronavirus fear sentiment progression, and outlines associated methods, implications, limitations and opportunities.
has issue date	2020-05-21(xsd:dateTime)
bibo:doi	10.3390/info11060314
has license	arxiv
sha1sum (hex)	494cb9024be445ad05b65e10ea5fa89a43f9ed09
schema:url	https://doi.org/10.3390/info11060314
resource representing a document's title	COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification
resource representing a document's body	covid:494cb9024be445ad05b65e10ea5fa89a43f9ed09#body_text
is schema:about of	named entity 'ESSENTIAL' named entity 'SHORT' named entity 'LOGISTIC REGRESSION' named entity 'PROGRESS' named entity 'ITS' »more»

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2025 OpenLink Software