Twitter Sentiment Analysis Based on Daily Covid-19 Table in Turkey

Abstract

The coronavirus pandemic, which began to affect the whole world in early 2020, has become the most talked about agenda item by individuals. Individuals announce their feelings and thoughts through various communication channels and receive news from what is happening around them. One of the most important channels of communication is Twitter. Individuals express their feelings and thoughts by interacting with the tweets posted. This study aims to analyze the emotions of the comments made under the "daily coronavirus table" shared by the Republic of Turkey Ministry of Health and to measure their relationship with the daily number of cases and deaths. In the study, emotional classification of tweets was implemented using LSTM, GRU and BERT methods from deep learning algorithms. The results of all three algorithms were compared with the daily number of cases and deaths.

Classification of Imbalanced Offensive Dataset – Sentence Generation for Minority Class with LSTM

“…LSTM with the repetitive module which has four operations (3 sigmoid and 1 tanh) provides memorization of information for these long time steps [49]. LSTM is a special RNN architecture with its hidden neurons composed of a memory cell and three gates namely the forget gate, input gate and output gate [50,51].…”

Evrişimsel Sinir Ağları ile Görüntülerde Gürültü Türünü Saptama

“…Günümüzde birçok sorunun çözümü için derin öğrenme mimarilerinin kullanımı yaygınlaşmıştır. Görüntü tanıma ve sınıflandırmada sağlık [8], finans [9], savunma [10], tarım [11] ve çevre [12] gibi farklı alanlarda derin öğrenme mimarilerinin kullanımı tercih edilir olmuştur. Son yıllarda büyük ilerleme kaydeden derin öğrenme algoritmalarından ESA, bilgisayarla görü, konuşma işleme gibi çeşitli alanlarda farklı görüntü tanıma görevlerinde kullanılmaktadır [13].…”