## 6 Conclusions and Future Work  
In this paper, we compare two statistical models to conduct opinion mining based on Infodemiology. The best result shown an accuracy of 90.80%, with sequences of five-char-grams applying random-forest. The word-gram model, on the other hand, achieved an accuracy of 90.50% with unigrams applying the support vector machines. Both models, char-grams and word-grams, achieved their best results with a subset (corpus-3) of the whole corpus, composed by the 400 tweets with more consensus among the volunteers. In addition, the analysis showed that the addition of bigrams or trigrams to the word-gram model does not significantly improves its accuracy. The same behaviour is detected in the char-gram model, where the best results are obtained by the use of fixed sequences of char-grams of length five. The combinations of larger sequences and combinations of char-grams do not improve the accuracy. We conclude that it is more important to compile a corpus with a certain degree of consensus than a large one. In addition, Infodemiology systems require us to measure the capability of predicting possible outbreaks in short and medium term. However, as the infectious diseases domain is very complex, we are planning to use other domains, such as weather or stock-market, to measure the reliability of creating predictive models for other domains.  
As future work, we will introduce neutral tweets to observe if the accuracy of the statistical-models is affected for each machine-learning classifier. However, in the infectious diseases domain, it is very difficult to distinguish between neutral or negative tweets because news related to an outbreak are usually perceived as negative, so it is difficult to keep the balanced corpus. In addition, we will compare these statistical-models with a linguistic-model in order to use psychology-relevant linguistic features instead of simple grams or char-grams with the expectation to increase the interpretability of the models.  
The methods described in this work will be used to create an automatic tool capable of monitoring infectious-diseases trends. We are now exploring three different areas: (1) social-networks, as described in this paper; (2) official documents, where we will apply NLP tasks, such as document-classification, named-entity recognition, and opinion mining; and (3) gamification techniques, to promote citizen participation, from identifying possible causes of infectious diseases, such as poor sanitary conditions or the presence of mosquitoes in the nearby areas. These data-sources will be available for health-care professionals through a dashboard capable of monitoring different geographical areas. This project also involves the application of ontologies to model the infectious diseases domain, and we will explore the reliability of block-chain, to create a trust-system of anonymous data sources.  
**Author Contributions:** conceptualization.A.-A., J.A.G.-D. and R.V.-G.; data curation, J.M.-M. and H.L.-A.; funding acquisition, R.V.-G.; investigation, O.A.-A.; project administration, R.V.-G.; resources, O.A.-A., J.M.-M. and H.L.-A.; Software, J.A.G.-D.; supervision, R.V.-G.; visualization, J.A.G.-D.; writing--original draft, all.  
**Funding:** This research was funded by the Spanish National Research Agency (AEI) and the European Regional Development Fund (FEDER / ERDF) through project KBS4 FIA (TIN2016-76323-R). This research has been also supported by CDTI through project CollaborativeHealth (UNI-20180001/IDI-20180989)  
**Conflicts of Interest:** The authors declare no conflict of interest.