Predicting Vaccine Hesitancy and Vaccine Sentiment Using Topic Modeling and Evolutionary Optimization Gokul S. Krishnan 1,2(B) , S. Sowmya Kamath 1 , and Vijayan Sugumaran 3 1 Healthcare Analytics and Language Engineering (HALE) Lab, Department of Information Technology, National Institute of Technology Karnataka, Surathkal, India sowmyakamath@nitk.edu.in 2 Robert Bosch Centre for Data Science and Artificial Intelligence, Indian Institute of Technology Madras, Chennai, India 010917@imail.iitm.ac.in 3 Department of Decision and Information Sciences, School of Business Administration, Oakland University, Rochester, USA sugumara@oakland.edu Abstract. The ongoing COVID-19 pandemic has posed serious threats to the world population, affecting over 219 countries with a staggering impact of over 162 million cases and 3.36 million casualties. With the availability of multiple vaccines across the globe, framing vaccination policies for effectively inoculating a country’s population against such diseases is currently a crucial task for public health agencies. Social net- work users post their views and opinions on vaccines publicly and these posts can be put to good use in identifying vaccine hesitancy. In this paper, a vaccine hesitancy identification approach is proposed, built on novel text feature modeling based on evolutionary computation and topic modeling. The proposed approach was experimentally validated on two standard tweet datasets – the flu vaccine dataset and UK COVID-19 vac- cine tweets. On the first dataset, the proposed approach outperformed the state-of-the-art in terms of standard metrics. The proposed model was also evaluated on the UKCOVID dataset and the results are pre- sented in this paper, as our work is the first to benchmark a vaccine hesitancy model on this dataset. Keywords: Evolutionary computation · Machine learning · Natural language processing · Population health analytics · Topic modeling 1 Introduction In the last few decades, the world has faced several epidemics and contagious viral diseases such as SARS, MERS, H1N1, Zika, Ebola etc., currently superceded by G. S. Krishnan—Work done as part of doctoral research work at HALE Lab, NITK Surathkal. c  Springer Nature Switzerland AG 2021 E. M´etais et al. (Eds.): NLDB 2021, LNCS 12801, pp. 255–263, 2021. https://doi.org/10.1007/978-3-030-80599-9_23