All Negative on the Western Front: Analyzing the Sentiment of the Russian News Coverage of Sweden with Generic and Domain-Specific Multinomial Naive Bayes and Support Vector Machines Classifiers

All Negative on the Western Front: Analyzing the Sentiment of the Russian News Coverage of Sweden with Generic and Domain-Specific Multinomial Naive Bayes and Support Vector Machines Classifiers

This thesis explores to what extent Multinomial Naive Bayes (MNB) and Support Vector Machines (SVM) classifiers can be used to determine the polarity of news, specifically the news coverage of Sweden by the Russian state-funded news outlets RT and Sputnik. Three experiments are conducted.  In the fi...

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Bibliographic Details
Main Author: Michel, David
Format: Others
Language:English
Published: Uppsala universitet, Institutionen för lingvistik och filologi 2021
Subjects:
sentiment analysis
news sentiment
text classification
cross-domain sentiment classification
domain specificity
domain-transfer problem
transfer learning
knowledge transfer
support vector machines
SVM
multinomial naive Bayes
Sweden
Aurora 17
Russia
Russian news
RT
Sputnik
cyberwarfare
influence campaign
disinformation
fake news
propaganda
Language Technology (Computational Linguistics)
Språkteknologi (språkvetenskaplig databehandling)
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447398

Internet

http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447398

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