國立虎尾科技大學 |

Classifying COVID-19 News on Sina Weibo.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Classifying COVID-19 News on Sina Weibo./
Author:	Sen, Arunachal.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	74 p.
Notes:	Source: Masters Abstracts International, Volume: 83-05.
Contained By:	Masters Abstracts International83-05.
Subject:	Linguistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28721308
ISBN:	9798480643633

Classifying COVID-19 News on Sina Weibo.
Sen, Arunachal.

Classifying COVID-19 News on Sina Weibo. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 74 p.

Source: Masters Abstracts International, Volume: 83-05.

Thesis (Master's)--University of Washington, 2021.

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

This thesis addresses the classification of Sina Weibo news related to the COVID-19 pandemic by using sentiment analysis. The design is a comparison study, involving four different systems. The systems were chosen after extensive reading on different approaches to sentiment analysis in Mandarin Chinese. These systems are neural network, k-medoids, HMM, and SVM. The core work of this thesis was in fine-tuning these systems to work with a small dataset of less than 3,000 examples. The final results from numerous experiments showed that the SVM and HMM systems achieved the highest results, followed by the neural network and k-medoids systems. Findings of this study showed that keyword frequency within a news category is not necessarily sufficient to ensure correct classification. Also, posts with names and satire remain challenging to classify and could be investigated further.

ISBN: 9798480643633Subjects--Topical Terms:

557829
Linguistics.
Subjects--Index Terms:

HMM

Classifying COVID-19 News on Sina Weibo.
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300 $a 74 p.
500 $a Source: Masters Abstracts International, Volume: 83-05.
500 $a Advisor: Levow, Gina-Anne.
502 $a Thesis (Master's)--University of Washington, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a This thesis addresses the classification of Sina Weibo news related to the COVID-19 pandemic by using sentiment analysis. The design is a comparison study, involving four different systems. The systems were chosen after extensive reading on different approaches to sentiment analysis in Mandarin Chinese. These systems are neural network, k-medoids, HMM, and SVM. The core work of this thesis was in fine-tuning these systems to work with a small dataset of less than 3,000 examples. The final results from numerous experiments showed that the SVM and HMM systems achieved the highest results, followed by the neural network and k-medoids systems. Findings of this study showed that keyword frequency within a news category is not necessarily sufficient to ensure correct classification. Also, posts with names and satire remain challenging to classify and could be investigated further.
590 $a School code: 0250.
650 4 $a Linguistics. $3 557829
650 4 $a Computer science. $3 573171
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653 $a K-medoids
653 $a Neural network
653 $a Sentiment analysis
653 $a SVM
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690 $a 0984
710 2 $a University of Washington. $b Linguistics. $3 1184011
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793 $a English
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