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Avrupa'yı etkisi altına aldığından beri Covid-19 salgını, özellikle Amerika kıtasında hızla yayılmaya devam etmektedir. Güncel verilere bakıldığında virüs yaklaşık 250 milyon insanı etkilemiş ve beş milyondan fazla insanın ölümüne neden olmuştur. Özellikle Avrupa kıtasında salgının hızla yayılmasıyla birlikte bu konu sosyal medyada tartışılmaya başlanmıştır. Özellikle Twitter bu çalışma alanında en sık kullanılan mikroblogdur. Bu çalışmada, küresel COVID-19 salgını sırasında Twitter üzerinden birçok kişi, kuruluş ve devlet kurumu tarafından paylaşılan tweetlerin VADER Duygu Analizi yöntemi kullanılarak, duygu analizi gerçekleştirilmesi amaçlanmaktadır. Bu çalışmada #covid19, #Covid, #pandemic, #social-distance, #socialdistance, #covid-19, #corona-virius, #coronavirus, #Chinesevirus, #Chinese-virus hashtagleri kullanılmıştır. Bu hashtag'ler ile 1 Ocak 2020 ile 1 Temmuz 2020 tarihleri arasında Twitter'dan toplam 60.243.040 tweet toplanmıştır. Bu çalışmada, Covid-19 ile ilgili Twitter verilerinde ifade edilen duyguları sınıflandırmak için VADER kullanılmış ve ortaya çıkan tweetlerin bileşik puanları, çok olumlu, olumlu, nötr, olumsuz, çok olumsuz olmak üzere beş kategoriye ayrılmıştır. Ayrıca çalışmada, aylık olarak en sık toplanan metin verilerinin görselleştirilmesi için Wordcloud kullanılmış ve tweetlerin içeriğini daha iyi anlamak için tweetlere N-gram uygulanmıştır. Çalışmada elde edilen sonuçlar incelendiğinde, çıkışın farklı dönemlerinde Covid-19 ile ilgili paylaşılan tweetlerin farklı duygusal durumları yansıtması oldukça ilginçtir.
Since it has been affected by Europe, the Covid-19 epidemic has continued to spread rapidly, especially in the U.S. continent. According to current data, the virus affected about 250 million people and caused more than five million deaths. Especially with the rapid spread of the epidemic in the European continent, this issue has begun to be discussed on social media. Twitter is the most commonly used microblog in this field. This study aims to carry out emotional analysis of tweets shared by many people, organizations and government agencies through Twitter during the global COVID-19 epidemic using the VADER Emotional Analysis method. This study used #covid19, #Covid, #pandemic, #social-distance, #socialdistance, #covid-19, #corona-virius, #coronavirus, #Chinesevirus, #Chinese-virus hashtags. This hashtag has collected a total of 60,243.040 tweets from Twitter between 1 January 2020 and 1 July 2020. In this study, VADER used to classify the emotions expressed in the Twitter data related to Covid-19 and the composite points of the emerging tweets were divided into five categories: very positive, positive, neutral, negative, very negative. In addition, the study used Wordcloud to visualize the most frequently collected text data per month and applied N-grams to tweet to better understand the content of tweets. When examining the results obtained in the study, it is quite interesting that shared tweets about Covid-19 in different periods of release reflect different emotional conditions.
The Covid-19 outbreak, which has been under the influence of Europe since then, continues to spread rapidly especially in the American continent. Looking at the current data, the virus has affected about 250 million people and has killed more than five million people. Especially with the rapid spread of the outbreak in the European continent, this issue started to be discussed in social media. In particular, Twitter is the most frequently used micro-blogging in this workspace. In this study, it is aimed to analyze the tweets shared by many people, organizations and government agencies through Twitter during the global COVID-19 outbreak with sentiment analysis using the VADER Sentiment Analysis method. The hashtags #covid19, #Covid, #pandemic, #social-distancing, #socialdistance, #covid-19, #corona-virius, #coronavirus, #Chinesevirus, #Chinese-virus were used in this study. With these hashtags, a total of 60,243,040 tweets were collected from Twitter between January 1, 2020 and July 1, 2020. In this study, we use the VADER to classify the sentiments expressed in Twitter data related to Covid-19 and the compound scores of the resulting tweets were divided into five categories: Highly Positive, Positive, Neutral, Negative, Highly Negative. In addition, in the study, the Wordcloud was used to visualize the most frequently collected text data monthly, and N-grams were applied to the tweets to better understand the content of the tweets. When the results obtained in the study are examined, it is quite interesting that the tweets shared about Covid-19 in different periods of the release reflect different sentimental situations.
Journal Type : Uluslararası
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