Infodemiology is the process of mining unstructured and textual data so as to provide public health officials and policymakers with valuable information regarding public health. The appearance of this new data source, which was previously unimaginable, has opened up a new way in which to improve public health systems, resulting in better communication policies and better detection systems. However, the unstructured nature of the Internet, along with the complexity of the infectious disease domain, prevents the information extracted from being easily understood. Moreover, when dealing with languages other than English, for which some of the most common Natural Language Processing resources are not available, the correct exploitation of this data becomes even more difficult. We intend to fill these gaps proposing an ontology-driven aspect-based sentiment analysis with which to measure the general public’s opinions as regards infectious diseases when expressed in Spanish by employing a case study of tweets concerning the Zika, Dengue and Chikungunya viruses in Latin America. Our proposal is based on two technologies. We first use ontologies in order to model the infectious disease domain with concepts such as risks, symptoms, transmission methods or drugs, among other concepts. We then measure the relationship between these concepts in order to determine the degree to which one concept influences other concepts. This new information is subsequently applied in order to build an aspect-based sentiment analysis model based on statistical and linguistic features. This is done by applying deep-learning models. Our proposal is available on a web platform, where users can see the sentiment for each concept at a glance and analyse how each concept influences the sentiment of the others.

信息流行病学是挖掘非结构化文本数据的过程，以便为公共卫生官员和决策者提供有关公共卫生的有价值的信息。这种以前难以想象的新数据源的出现，为改善公共卫生系统开辟了一条新途径，从而产生更好的沟通政策和更好的检测系统。然而，互联网的非结构化性质以及传染病领域的复杂性使得提取的信息不易被理解。此外，在处理英语以外的语言时，一些最常见的自然语言处理资源不可用，正确利用这些数据变得更加困难。我们打算填补这些空白，提出一种本体驱动的基于方面的情感分析，通过对拉丁美洲有关寨卡病毒、登革热病毒和基孔肯雅病毒的推文进行案例研究，衡量公众对用西班牙语表达的传染病的看法。我们的建议基于两种技术。我们首先使用本体论来对传染病领域进行建模，其中包括风险、症状、传播方法或药物等概念。然后，我们测量这些概念之间的关系，以确定一个概念影响其他概念的程度。随后应用这些新信息来构建基于统计和语言特征的基于方面的情感分析模型。这是通过应用深度学习模型来完成的。我们的提案可在网络平台上使用，用户可以一目了然地看到每个概念的情绪，并分析每个概念如何影响其他概念的情绪。