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    Sentiment Analysis of Covid-19 Tweets by using LSTM Learning Model
    (Ali KARCI, 2021) Karaca, Yunus Emre; Aslan, Serpil
    Social media plays an important role in our lives due to the conditions of the age we live. Nowadays, the most popular social media platform that prioritizes meaningful content sharing is Twitter. In Twitter, which produces big data on an unprecedented scale, users have the opportunity to share their own perspectives, feelings, and experiences, as well as examine the opinions of other individuals. The Coronavirus-2019 (Covid-19) disease, transmitted through close contact and small droplets spread by people coughing, sneezing, or speaking, has created social and economic wounds worldwide. As of July 7, 2021, more than 185 million people worldwide have been diagnosed with the New Coronavirus (Covid-19), and approximately 4 million people have died from this infectious disease. This work focuses on the analysis of the sentiments that Covid-19 leaves on people, using the tweets that people share about the Covid-19 pandemic on the Twitter platform. Analyzes are based on deep learning algorithms. Sentiment analysis can provide serious benefits. In this study, we used a Long-short Term Memory (LSTM) based network model. Also, we compared the proposed model other machine learning algorithms: Support Vector Machine (SVM), Naïve Bayes and Logistic Regression. Experimental results show that our proposed method can effectively perform sentiment analysis on the Twitter dataset.