## 3 Methods
### Data Acquisition  
The creation of healthcare web-based data involves mainly two steps. The first step consisted of the data acquisition, whereas the second step involved human intervention for classifying and checking the results. Machine learning techniques required an existing labelled corpus to work. The manual classification and labelled of a corpus is a very time-consuming task. Distant supervision is a technique that consists of the automatic classification of the texts based on certain hypothesis [23] such as the presence of emoticons for satire identification. However, distant-supervision is not always possible. We used Twitter, a popular micro-blogging platform, as the data-provider for corpus compilation. In Twitter, users can create and share their opinions in micro-posts of 248 characters maximum known as tweets. In Twitter, (1) posts are public and easy to obtain by using Twitter API. In addition, (2) due to the text limit of the posts, it is easy to spot posts focused in one specific topic. These two facts has leaded Twitter to be widespread used for text-analysis experiments. We used a self-developed tool that uses Twitter API to obtain, systematically, tweets that contain the terms related to neglected tropical diseases: zika, chinkungunya, malaria and dengue. These tweets were compiled from some countries of Central America, with the geographical coordinates (\(-\)0.1596997, \(-\)78.452125313) with a radius of 1500 kilometres. This area includes the countries of Central America such as Ecuador, Colombia, and Panama. It also includes some areas from Venezuela, Peru, and Brasil (see Figure 1). Then, we apply a normalization process to the tweets consisted in transform some entities, such as hyperlinks, to fixed tokens and to remove duplicated items. As no reliable techniques for distant-supervision were identified, we relied in a manual classification. Tweets were manually classified by a total of 20 volunteers from the University of Guayaquil. Volunteers were provided with a manual of the tool, and with some examples concerning what kind of statements are expected to be positive, negative, neutral, and out-of-context tweets. During the classification stage, the performance of the volunteers were monitored in order to identify possible issues as soon as possible. Each week, the performance of each volunteer was measured individually to ensure that the number and quality of the ratings were constant. The overall performance of the volunteers among time is shown in Figure 2 that illustrate the classification process. We can observe that the work was continuous during the weekdays. Each tweet was rated individually by different volunteers. This strategy allowed us to obtain a global score that indicates the level of agreement, or disagreement, among the volunteers. A screen-capture of this tool is shown in Figure 3. In a nutshell, the volunteers opened the tool using a log-in system. Once the volunteers were authenticated, the system generated a random list of tweets still unrated by him. Then, each volunteer can rate each tweet assigning one option from a  
Figure 1: Geographical area from Central America where tweets were collected. Figure 2: Volunteers performance over time. Each colour represents a different classification, where green represents positive tweets, red represents negative tweets and grey colours represents neutral and out-of-context tweets. The horizontal axis is measured with GMT+01:00.  
drop-down list. The texts were normalized version of the tweets, without images nor links. However, it was possible to volunteers to visualize the original tweet from Twitter. However, volunteers were discouraged to do it because we were only interested in the sentiment of the text, but not the sentiment of external resources or media. As a result, a balanced corpus composed by 3082 positive tweets and 3082 negative tweets was obtained. In total, the corpus contains 3719 different accounts from Twitter with a mean of 1.39 tweets per account with a standard deviation of 0.98.