Social media has become a relevant information source for several decision-making processes and for the definition of business strategies. As various sentiment analysis techniques are used to transform collected data into intelligence information, the sentiment classifiers used in these collection environments must be carefully studied and observed before being considered trustful and ready to be installed in decision support systems. An important research area concerns the robustness of sentiment classifiers in view of new adversarial attacks, in which small perturbations may be created by malicious users to deceive the sentiment classifiers, generating a perception different from the one that should be observed in the environment. Thus, it is important to identify and analyze the vulnerabilities of these classifiers under different strategies of adversarial attacks to propose countermeasures that can be used to mitigate such attacks. In this context, this work presents adversarial attacks related to a lexical natural language classifier. Being the target of the attacks, this classifier is used to calculate the sentiment of collected data as posted by users in various social media applications. The results indicate that the found vulnerabilities, if exploited by malicious users in applications that use the same lexical classifier, could invert or cancel the classifiers’ perception, thus generating perceptions that do not correspond to the reality for decision making. This work also proposes some countermeasures that might mitigate the implemented attacks.

社交媒体已成为一些决策过程和业务策略定义的相关信息源。由于使用了各种情感分析技术将收集的数据转换为情报信息，因此在被认为可信任并准备安装在决策支持系统中之前，必须仔细研究和观察这些收集环境中使用的情感分类器。一个重要的研究领域是关于情感分类器的鲁棒性，因为它具有新的对抗性攻击，其中恶意用户可能会产生小的干扰来欺骗情感分类器，从而产生与环境中应观察到​​的感知不同的感知。因此，重要的是要识别和分析这些分类器在对抗性攻击的不同策略下的脆弱性，以提出可用于减轻此类攻击的对策。在这种情况下，这项工作提出了与词汇自然语言分类器有关的对抗性攻击。作为攻击的目标，此分类器用于计算用户在各种社交媒体应用程序中发布的收集数据的情绪。结果表明，如果发现的漏洞被恶意用户在使用相同词法分类器的应用程序中利用，则可能会颠倒或取消分类器的感知，从而生成与决策现实不符的感知。这项工作还提出了一些可能减轻实施的攻击的对策。