Please use this identifier to cite or link to this item: https://hdl.handle.net/11499/56895
Title: Emotions on Social Media: A Sentiment Analysis Approach Based on Twitter (X) Data on the Russian-Ukraine War
Authors: Temel Eginli, Ayşen
Taş, Neslihan Özmelek
Abstract: Twitter (X) is an important tool that reflects the feelings and attitudes of the public. For this reason, in this study, especially when it comes to events that concern society, Twitter provides an opportunity to both follow the agenda and understand the reactions through instant sharing. Twitter is a social media platform that allows the public to convey their feelings, thoughts, and attitudes to the masses. Twitter provides the opportunity to stay up-to-date and understand reactions through instant posts, especially for social events. In this research, Twitter posts made with the Ukraine hashtag between March 1 and April 30, 2022, during the Russia-Ukraine War, were eliminated with the "war" filter, and the expressions were analyzed using the sentiment analysis method. Various URLs were eliminated, and research was carried out on ten thousand tweets. The tweets obtained were categorized as positive, negative, and neutral. Accordingly, the expressions containing positive, negative, and neutral emotions were analyzed by determining the emotional inferences of the words in the tweets with an artificial intelligence algorithm and then detailed by the researchers with content analysis. In this sense, this study becomes important in understanding how the masses express their reactions through emotional social media platforms and what their emotions are in this process. Therefore, this research can be a clue for the consequences of international war on the masses.
URI: https://doi.org/10.37093/ijsi.1336016
https://search.trdizin.gov.tr/yayin/detay/1218357
https://hdl.handle.net/11499/56895
ISSN: 1307-8364
1307-9999
Appears in Collections:İletişim Fakültesi Koleksiyonu
TR Dizin İndeksli Yayınlar Koleksiyonu / TR Dizin Indexed Publications Collection

Files in This Item:
File SizeFormat 
10.37093-ijsi.1336016-3301026.pdf534.5 kBAdobe PDFView/Open
Show full item record



CORE Recommender

Page view(s)

40
checked on Aug 24, 2024

Download(s)

30
checked on Aug 24, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.