DEAR EDITOR,

Social networking sites like Twitter have been a prevalent option for individuals to express their opinions, report real-life events and provide visions into what is happening around the world.In the epidemic of COVID-19, individuals have used Twitter to spontaneously share data visualization from news media and government agencies and send visualized data images they have generated separately1.

Microblogging websites have evolved to develop a source for a variety of information. This is because of the nature of the microblogs in which individuals post real-time messages about their views on various topics, discuss current issues, complaints, and express their sentiment about events in everyday life2.

Sentiment analysis is the field of research that evaluatespublic's opinions, feelings, assessments, attitudes, and feelings from writing language. It is one of the active areas of natural language processing and is extensively studied in data mining, web mining and text mining3.

This research has expanded into management and social sciences outside of computer science because of its impact to the whole society. The rising importance of sentiment analysis is evolving as social network evolves, such as reviews, forum discussions, blogs, microblogs, Twitter and social media4.

Microblogging data such as Twitter, where users messages real-time feedback and statements almost about "everything", make newer and various challenges. Some of the studies on sentiment analysis of Twitter data wereconducted by Go et al5, and Pak and Paroubek6. Go et al. used distance learning to collect thesentiment data. They used tweets ending in positive emotions like “:)” “: -)” as positive and negative emotions like “: (” “: -(” as negative5.

Another study for sentiment analysis on Twitter data wasreported by Barbosa and Feng4. They applied of syntax features of tweets like a retweet, hashtags, link, punctuation and exclamation marks for the sentiment analysis.

In this work, we investigated one of the famous microblogs in Twitter to find out how the others think about the vaccinesof new Coronavirus. We have implemented a fuzzy rule-based evolutionary algorithm called Eclass1-MIMO7 and then built three classify "tweets" into positive (hopeful and optimism to control this epidemic), negative (frustration, stress, and anxiety), and neutral (apathetic and trivial for the user) sentiment.

The search was implemented using the keywords as follows: coronavirus, ncov, COVID-19, virus, 2019-nCoV, vaccine, immunization, Pfizer corona vaccine, Moderna vaccine, AstraZeneca vaccine, coronavirus outbreak, Epidemic, only or in combination, in English language. In total, 1,126,137 tweet about vaccine of covid-19 were extracted between 01 December and 30 of December 2020. Figure 1 shows the results obtained from the monitoring of a vaccine of covid-19 related news within the period of this study.

Classifying "tweets" into positive sentiment means that the people whit the discovery of the Covid-19 vaccine has given them a sense of salvation, hope for an end to the Corona pandemic and a return to pre-pandemic life.This group tends to be vaccinated if the vaccine is available

Classifying "tweets" into negative sentiment means that people feel cowardly and distrustful of the Covid-19 vaccine and are reluctant to vaccinate if the vaccine is available.

Classifying "tweets" into neutral sentiment group means that people did not feel happy or scared about the news of the discovery of the Covid-19 vaccine.

The result of this study showed that 591053 tweet (52%) were in positive sentiment, 382431 (34%) neutral sentiment and 152653(14%) negative sentiment about vaccine of covid-19.

The question, is that why's people, despite have knowledge that COVID-19 is spread in all the world, and lead to hospitalization or deaths, but they do not welcome vaccination and are afraid to do so (negative and neutral groups).

The key principle is that each individuals and communities should identify and practice good lifestyles, maintain health and try to prevent the spread of virus by a proper behavior training, and consider the role of health education and social marketing8,9.

Social marketing in public health, the use of marketing to design and implement programs to promote socially beneficial behavior change, has grown in popularity and usage within the public health community[10].Using social marketing principles that include product, place, and promotion by health professionals to change the community's behavior toward supporting and embracing the Covid-19 vaccination program can help achieve high coverage to a level of herd immunity.

Furthermore increasing the effectiveness