Sentiment Analysis of Covid-19 Tweets by using LSTM Learning Model

Year 2021, Volume: IDAP-2021 : 5th International Artificial Intelligence and Data Processing symposium Issue: Special, 366 - 374, 20.10.2021

Yunus Emre Karaca , Serpil Aslan

https://doi.org/10.53070/bbd.990421

Cited By: 3

Abstract

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.

Keywords

Twitter, Sentiment Analysis, Coronavirus, Deep Learning

LSTM Öğrenme modeliyle COVID-19 Tweetleri üzerinde Duygu Analizi

Abstract

Sosyal medya, yaşadığımız çağın şartlarından dolayı hayatımızda önemli bir rol oynamaktadır. Twitter, günümüzde anlamlı içerik paylaşımına öncelik veren en popüler sosyal medya platformudur. Benzeri görülmemiş ölçekte büyük veri üreten Twitter üzerinde, kullanıcılar hem kendi bakış açılarını, duygularını, deneyimlerini paylaşabilme imkânı bulmakta hem de diğer bireylerin görüşlerini inceleyebilmektedir. Yakın temas ve insanların öksürme, hapşırma veya konuşma esnasında yaydığı küçük damlacıklar yoluyla bulaşan 2019 Koronavirüs (Covid-19) hastalığı tüm dünyada sosyal ve ekonomik yaralar oluşturdu. 7 Temmuz 2021 tarihi itibariyle, tüm dünyada 185 milyondan fazla kişiye Yeni Koronavirüs (Covid-19) teşhisi konuldu ve yaklaşık 4 milyon kişi bu bulaşıcı hastalık sebebiyle hayatını kaybetti. Bu çalışmada, Twitter platformu üzerinden insanların Covid-19 pandemisi ile ilgili paylaştığı twitleri kullanarak Covid-19’un insanlar üzerinde bıraktığı duyguların analizine odaklanmaktadır. Analizler derin öğrenme algoritmalarına dayanmaktadır. Duygu analizi bazen ciddi faydalar sağlayabilir. Bu çalışmada tekil etiket-çoğul sınıf yaklaşımı ile ikili bir sınıflandırma yapılmıştır. Çalışmada LSTM ağı ve Word2Vec öğrenimi modelleri test edildi. Model, bir LSTM ağı kullanılarak kurulmuştur. Deneysel sonuçlar, önerilen yöntemimizin Twitter veri seti üzerinde etkin bir şekilde duygu analizi yapılabileceğini göstermektedir.

Keywords

Twitter, Duygu Analizi, Coronavirüs, Derin Öğrenme

References

• Internet Users Worldwide Statistic, Available at: https://www. broadbandsearch.net/blog/internet-statistics, Anonymous, retrieved 28th July, 2021.
• He, W., Wu, H., Yan, G., Akula, V., & Shen, J. “A novel social media competitive analytics framework with sentiment.” Elsevier, 1-12, 2015.
• Twitter. (2021, 07 13). wikipedia:https://tr.wikipedia.org/wiki/Twitter
• Fang, X., & Justin, Z., “Sentiment analysis using product review data.” Journal of Big Data, 1-14, 2015.
• Christi, J., & Jain, G., “Sentiment Categorization through Natural Language Processing :A Survey.”, 104-107, (2019, 11 15).
• Chakraborty, K., Bhatia, S., Bhattacharyya, S., Platos, J., Bag, R., & Hassanien, A. E., “Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media.” Elsevier, 2020.
• Alrazaq, A. a., Alhuwail, D., Househ, M., Hamdi, M., & Shah, Z., “Top Concerns of Tweeters During the COVID-19 Pandemic: Infoveillance Study.” JOURNAL OF MEDICAL INTERNET RESEARCH, 1-10, 2020.
• Gencoglu, O., “Large-Scale, Language-Agnostic Discourse Classification of Tweets During COVID-19.” Machine Learning and Knowledge Extraction, 603–616, 2020.
• Samuel, J., Ali, G. G., Rahman, M., Esawi, E., & Samuel, Y., “Covid-19 public sentiment insights and machine learning for tweets classification.” Information, 11(6), 314, 2020.
• Iyer, P., & Kumaresh, S., “Twitter Sentiment Analysis On Coronavirus Outbreak Using Machine Learning Algorithms.” European Journal of Molecular & Clinical Medicine, 2663-2676, 2020.
• BİLEN, B., & HORASAN, F., “LSTM network based sentiment analysis for customer reviews.” JOURNAL of POLYTECHNIC, 2021.
• Türkmenoğlu, C., & Tantuğ, A. C., “Sentiment analysis in Turkish media.” Workshop on Issues of Sentiment Discovery and, (1-11), 2014.
• Liu, B., “Sentiment analysis and subjectivity.” Handbook of natural language processing, 2, 627-666, 2010.
• Xiao, Y., & Yin, Y., “Hybrid LSTM neural network for short-term traffic flow prediction.” Information, 10(3), 105, 2019.
• Loria S. textblob Documentation. Release 015. 2018; 2.
• Sohangir S, Petty N, Wang D., “Financial sentiment lexicon analysis.” In: 2018 IEEE 12th International Conference on Semantic Computing (ICSC), p. 286–289, 2018.
• Ankit and Saleena, N., “An ensemble classification system for twitter sentiment analysis.” Procedia Computer Science, 132(2):937–946, 2018.