A text mining analysis of the change in status of the Hagia Sophia on Twitter: the political discourse and its reflections on the public opinion

Demirel, Sadettin; Kahraman, Elif; Gündüz, Uğur

doi:10.1080/15456870.2022.2093354

Cited by 5 publications

(1 citation statement)

References 59 publications

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“…The developers also advise this way of classifying each tweet into sentiment categories of the VADER dictionary. Similar sentiment classifications were also applied in the scholarly works (Akerlund, 2020;Demirel, Kahraman, & Gündüz, 2022;Ribeiro, 2016) that benefited from VADER Similarly, Sentimentr is designed to calculate text polarity by considering valence shifters, and negators in the text block (Rinker, 2018). Sentimentr differs from VADER because the software package is not peer-reviewed and is exclusively developed for R programming language.…”

Section: Sentiment Analysis Dictionariesmentioning

confidence: 99%

Dünya Sağlık Örgütü’nün Covid-19 Pandemisi Öncesi ve Sırasında Twitter Kullanımı: Tweetlerin Duygu ve Metin Analizi

Demirel¹,

Gündüz

2022

Intermedia International E-Journal

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Social media as a form of mass self-communication offers a great deal of potential and opportunities for users and organizations to get, diffuse, redistribute, and monitor information and messages due to its features of accessibility, digitality, and time-space independence. Especially during a crisis, being able to harness the power of social media is an imperative management practice not only to provide the public with accurate information but also to deter misleading claims and rumors regarding the subject. Based on this assumption, this study aims to measure and compare the overall sentiment of the World Health Organization’s (WHO) tweets before and during the coronavirus pandemic. Along with the overall sentiment of tweets, the contents of tweets, and the WHO’s Twitter use practices before and during the pandemic were assessed in the framework of crisis management and health communication. Within the scope of our study, the WHO’s 34.673 tweets between 2018 and 2021 were examined with sentiment and textual analysis. The study also indicates the health policies before and during the covid19 pandemic and crisis management tactics by analyzing and close reading the WHO’s tweets.

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Section: Sentiment Analysis Dictionariesmentioning

confidence: 99%