東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Sentiment Analysis for Opinion Leade...

Mir, Reem Sajid.

Linked to FindBook

Google Book

Amazon

博客來

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

Record Type:	Electronic resources : Monograph/item
Title/Author:	Sentiment Analysis for Opinion Leaders on Twitter: A Case Study of COVID-19./
Author:	Mir, Reem Sajid.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	40 p.
Notes:	Source: Masters Abstracts International, Volume: 85-01.
Contained By:	Masters Abstracts International85-01.
Subject:	Datasets. -
Online resource:	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.
LDR:02190nmm a2200337 4500 001 2394899
005 20240513061041.5
006 m o d
007 cr#unu||||||||
008 251215s2023 ||||||||||||||||| ||eng d
020 $a 9798379955465
035 $a (MiAaPQ)AAI30662972
035 $a (MiAaPQ)BritishUnivDubai12342134
035 $a AAI30662972
040 $a MiAaPQ $c MiAaPQ
100 1 $a Mir, Reem Sajid. $3 3764395
245 1 0 $a Sentiment Analysis for Opinion Leaders on Twitter: A Case Study of COVID-19.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 40 p.
500 $a Source: Masters Abstracts International, Volume: 85-01.
500 $a Advisor: Shaalan, Khaled.
502 $a Thesis (M.S.)--The British University in Dubai, 2023.
506 $a This item must not be sold to any third party vendors.
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.
590 $a School code: 6608.
650 4 $a Datasets. $3 3541416
650 4 $a Sentiment analysis. $2 lcstt $3 3266790
650 4 $a Support vector machines. $3 2058743
650 4 $a Web studies. $3 2122754
650 4 $a Information technology. $3 532993
690 $a 0489
690 $a 0646
710 2 $a The British University in Dubai. $3 3701586
773 0 $t Masters Abstracts International $g 85-01.
790 $a 6608
791 $a M.S.
792 $a 2023
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30662972