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Sentiment Analysis for Opinion Leaders on Twitter : = A Case Study of COVID-19.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Sentiment Analysis for Opinion Leaders on Twitter :/
Reminder of title:	A Case Study of COVID-19.
Author:	Mir, Reem Sajid.
Description:	1 online resource (40 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-01.
Contained By:	Masters Abstracts International85-01.
Subject:	Datasets. -
Online resource:	click for full text (PQDT)
ISBN:	9798379955465

Sentiment Analysis for Opinion Leaders on Twitter : = A Case Study of COVID-19.
Mir, Reem Sajid.

Sentiment Analysis for Opinion Leaders on Twitter :A Case Study of COVID-19. - 1 online resource (40 pages)

Source: Masters Abstracts International, Volume: 85-01.

Thesis (M.S.)--The British University in Dubai, 2023.

Includes bibliographical references

The coronavirus or COVID-19 is an ongoing global problem where a pandemic was implemented early in 2020 during the outbreak. Social media platforms were used during the pandemic to share views and exchange information. This study aims to provide a framework for sentiment analysis of opinion leaders on Twitter. The experiments were conducted by aiming COVID-19 specific tweets from four opinion leaders by applying machine learning models. The dataset collected uses covid hashtags and tweets posted in English. Sentiment analysis are then performed on these tweets for analysis. The tweets are then preprocessed to prepare it for evaluation. This research provides findings from these tweets using sentiment analysis on machine learning models where the logistic regression model provided the best accuracy results followed by the Multi-layer perceptron model, Support vector machine, Convolutional neural network, and Decision tree. As the tweets directly affect people's thoughts, the purpose of these results was to know about the tweet's sentiments from diverse public opinion leaders around the world during COVID-19.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379955465Subjects--Topical Terms:

1372549
Datasets.
Index Terms--Genre/Form:

554714
Electronic books.

Sentiment Analysis for Opinion Leaders on Twitter : = A Case Study of COVID-19.
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504 $a Includes bibliographical references
520 $a The coronavirus or COVID-19 is an ongoing global problem where a pandemic was implemented early in 2020 during the outbreak. Social media platforms were used during the pandemic to share views and exchange information. This study aims to provide a framework for sentiment analysis of opinion leaders on Twitter. The experiments were conducted by aiming COVID-19 specific tweets from four opinion leaders by applying machine learning models. The dataset collected uses covid hashtags and tweets posted in English. Sentiment analysis are then performed on these tweets for analysis. The tweets are then preprocessed to prepare it for evaluation. This research provides findings from these tweets using sentiment analysis on machine learning models where the logistic regression model provided the best accuracy results followed by the Multi-layer perceptron model, Support vector machine, Convolutional neural network, and Decision tree. As the tweets directly affect people's thoughts, the purpose of these results was to know about the tweet's sentiments from diverse public opinion leaders around the world during COVID-19.
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