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Sentiment Analysis for Opinion Leade...

Mir, Reem Sajid.

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Sentiment Analysis for Opinion Leaders on Twitter: A Case Study of COVID-19./
作者:	Mir, Reem Sajid.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	40 p.
附註:	Source: Masters Abstracts International, Volume: 85-01.
Contained By:	Masters Abstracts International85-01.
標題:	Datasets. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30662972
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. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 40 p.

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

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

This item must not be sold to any third party vendors.

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.

ISBN: 9798379955465Subjects--Topical Terms:

3541416
Datasets.

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