Twitter Sentiment Analysis for Covid-19 Vaccines in Turkey based on BiGRU-CNN Deep Learning Model

Yıl 2022, , 312 - 330, 31.12.2022

Serpil Aslan

https://doi.org/10.29132/ijpas.1087486

Öz

Nowadays, social media platforms are the best way to express emotions. For nearly two years, the emergence of the Covid-19 new coronavirus epidemic has created unprecedented complex emotions on people in our country as well as all over the world. People's emotions became more complex after the start of vaccine studies against Covid-19. More recently, Covid-19's Delta, Omicron etc. The emergence of variants also created a great fear in the society again. In this process, people turned to social media tools to share their feelings and thoughts. Achieving sentiment analysis on Twitter is a very important and challenging task. The aim of this study is to investigate the different feelings of the Turkish people about the vaccination process by making use of the power of deep learning architectures and to provide an overview of the public's reactions to the current vaccination initiatives. In the study, Turkish tweets shared on Twitter between 16 June 2021 and 18 September 2021 were collected. People's feelings about vaccines of all kinds were assessed using TextBlob, a natural language processing (NLP) tool. Next, a new model for emotion classification was proposed. The proposed model is the BiGRU-CNN model using a single-layer Bi-directional Gateway Recurrent Unit (Bi-GRU) and Convolutional Neural Network (CNN) model with the Glove word embedding vector. The experimental results of the proposed method are promising when compared with the latest models. This work improves understanding of the public's views on COVID-19 vaccines and supports the goal of eradicating the coronavirus disease from the world.

Anahtar Kelimeler

Covid-19 vaccines, Sentiment Analysis, Deep Learning, Glove, CNN, BiGRU

BiGRU-CNN Tabanlı Derin Öğrenme Modeliyle Türkiye’deki Covid-19 Aşılarına Yönelik Twitter Duygu Analizi

Öz

Günümüzde, sosyal medya platformları duyguları ifade etmenin en iyi yoludur. Yaklaşık iki yıldır, Covid-19 yeni koronavirüs salgının ortaya çıkması tüm dünyada olduğu gibi ülkemizde de insanların üzerinde benzeri görülmemiş karmaşık duygular yarattı. Covid-19’a karşı aşı çalışmalarının başlamasından sonra insanların duyguları daha karmaşık hale geldi. Daha yakın zamanda, Covid-19’un Delta, Omicron vb. varyantlarının çıkması da toplumda yeniden büyük bir korku yarattı. İnsanlar, bu süreçte duygu ve düşüncelerini paylaşmak üzere Twitter gibi sosyal medya araçlarına yöneldi. Twitter’da duygu analizi yapmak çok önemli ve zorlu bir görevdir. Bu çalışmada amacımız, derin öğrenme mimarilerinin gücünden faydalanarak Türk halkının aşılama süreciyle ilgili farklı duygularını araştırmak ve halkın mevcut aşılama girişimlerine yönelik tepkilerine genel bir bakış sunmaktır. Çalışmada, Twitter’da 16 Haziran 2021 ve 18 Eylül 2021 arasında paylaşılan Türkçe tweetler toplanmıştır. İnsanların her türden aşılarla ilgili duyguları, doğal dil işleme (NLP) aracı olan TextBlob kullanılarak değerlendirildi. Daha sonra, duygu sınıflandırması için yeni bir model önerildi. Önerilen model, Glove kelime gömme vektörüyle tek katmanlı Çift-yönlü Geçitli Tekrarlayan Birim (Bi-GRU) ve Evrişimli Sinir Ağı (CNN) modelini kullanan BiGRU-CNN modelidir. Önerilen yöntemin deneysel sonuçları en son modellerle kıyaslandığında umut vericidir. Bu çalışma, halkın COVID-19 aşıları hakkındaki görüşlerinin anlaşılmasını geliştirmekte ve koronavirüsü dünyadan yok etme hedefini desteklemektedir.

Anahtar Kelimeler

Covid-19 aşı, Duygu Analizi, Derin Öğrenme, Glove, CNN, BiGRU

Kaynakça

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