## 1 Introduction  
Nowadays, information can be obtained from a vast number of sources, most of which are available on the Internet. It is well known that the Internet has become our main knowledge engine, redefining the way we communicate and gain understanding about the world around us. That implies that information of every kind and from every field can be publicly accessed just by typing a few words into our navigator. Among the wide range of knowledge areas, searches for information on the Internet related to healthcare are common. People can solve health doubts and find data in a quick and easy manner. If a certain health issue concerns the population, such searches tend to increase. In the past, some studiesabout these web queries have corroborated that fact [1; 2; 3] and have been used in the pursuit of improving public healthcare strategies and social wellbeing. Some of such health issues can be related to the early detection of disease outbreaks [4; 5; 6; 7], disease surveillance [8; 9; 10; 11] or epidemic intelligence [5; 12] and even in the current COVID-19 outbreak, infoveillance studies can aid in tackling the pandemic situation [13; 14; 15]. However, the diseases that have been screened under this type of analyses are not limited to COVID-19 as it will be discussed later. The Internet has allowed the emergence of new services and applications. Some of the most popular and with an increasing use, can be grouped under the term \"social media\". Social media is a subject of study for a broad variety of domains related to computer science, since it can be seen as an important means of data to analyze users' feelings and opinions. Those approaches that have taken advantage of the information present in social media have traditionally included political [16] and marketing campaigns [17; 18; 19; 20] or financial predictions [21; 22; 23]. Lately, it has been proven that in the health scope, social media information can be relevant too by assessing epidemiological patterns [24], predicting epidemic outbreaks [25; 26] or detecting drug side effects [27] and medication safety [28]. Moreover, concerns about vaccines and vaccination are widely expressed in social media [29; 30]. With the expansion of anti-vaccines movements in the recent years, the debate in social media has only grown and several conversations about vaccines have been monitored [31; 32; 33; 34]. Special controversy has surrounded the human papillomavirus (HPV) vaccines [29; 35; 36; 37; 38; 39; 40; 41; 42; 43]. Besides, some of the diseases that have been studied under these types of approaches of Internet and social media mining comprise largely Influenza [4; 5; 9; 11; 25; 44; 45; 46] but also others such as Zika virus disease [47; 48], cholera [24], obesity [49] or diabetes [50]. Twitter, amongst all the possibilities in social media, has been explored in numerous works, as it is the perfect platform to share opinions that can be mined [17; 19; 22; 23]. Sentiment analysis or opinion mining stands for natural language processing (NLP) methods that aim to computationally extract, interpret and classify emotions and subjective information from unstructured resources. The attitude of authors towards a topic in a text can be categorized as being positive, negative or neutral. In other words, we can assign a polarity to a text. The applications of these methods involve multiple domains (political science, social sciences, market research, etc.) and have evolved over time [51]. Nowadays, the most important sources to mine in this context come from the Internet and, as it has already been mentioned, Twitter can be seen as one of the most popular. The research on sentiment analysis in Twitter is called Tweet Sentiment Classification (TSC) [52] and multiple works have been developed under this approach [19; 20; 22; 23; 53]. One important topic that has attracted attention on Twitter has been vaccination.