# A Survey of Adversarial Attacks: An Open Issue for Deep Learning Sentiment Analysis Models
###### Abstract
Through the search engines the selected information sources, the query string was executed for all metadata in the advanced search section. Figure 3 depicts the process of extracting and filtering works proposing adversarial attack designs for the study task. Figure 3a illustrates the amount of articles retrieved in the data extraction stage by executing the query string. We obtained a total of 1040 articles related to adversarial attacks for text applications, including articles for sentiment analysis. First, the retrieved works were manually filtered by checking the year of publication, title, and abstract to exclude those that did not align with the research objectives of this work. As a result, the number of retrieved works was reduced to 51 (refer to Figure 3b). As a final step, the inclusion and exclusion criteria were applied, resulting in 33 papers being selected for the initial review. **Contributions of this survey. ** The objective of conducting this survey is to provide a comprehensive review of research efforts on adversarial example generation, specifically for deep neural networks used in sentiment analysis. In order to comprehend the importance of sentiment analysis in decision-making and knowing the scope that adversarial examples have on the results of deep learning models, it is necessary to understand the behavior of adversarial attacks with the aim of proposing defenses against them. We pursue this survey to serve researchers and practitioners interested in designing, attacking, and defending sentiment analysis deep neuronal networks. Although this work includes essential information concerning preliminary knowledge related to adversarial attacks, we expect that readers will have basic knowledge of sentiment analysis and deep neural network  
Figure 3: Paper selection methodology. After applying the query string, a total of 1040 papers related to adversarial attacks for text applications and sentiment analysis were retrieved in the data extraction stage. These papers were filtered by reviewing the year, title, and abstract, then applying the inclusion and exclusion criteria after which the total number of papers for initial review was reduced to 33. (**a**) Papers retrieved during the data extraction stage. A total of 1040 works were obtained for text applications, including works addressing sentiment analysis attack. The green circles highlight the primary works from which others were derived according to our query string. (**b**) The papers obtained during data extraction were filtered by checking the year, title, and abstract to validate the period and determine whether they corresponded to the topics of adversarial attacks and sentiment analysis. A total of 51 papers remained after filtering. Application of the inclusion and exclusion criteria further reduced the total number of papers for initial review to 33.  
architectures, which are not the focus of this article. To summarize, the main contributions of this survey are:  
* We collect, select, review, summarize, and discuss adversarial attacks, especially those suited to sentiment analysis models, with the objective of providing an information framework for current adversarial attacks methods that expose vulnerabilities of sentiment analysis models. This is the first survey to mainly focus on adversarial attacks for sentiment analysis. The aim is to assist researchers in addressing new vulnerabilities and developing defense methods to mitigate the negative consequences that can result from incorrect results produced by sentiment analysis models. * We provide essential information concerning preliminary knowledge related to adversarial attacks, which allows this work to be self-contained. * We draw on the reviewed literature to discuss open issues in adversarial example design for sentiment analysis deep neuronal network models, and identify possible new research directions concerning defenses for sentiment analysis models based on the issues and challenges identified during this review. The rest of this document is organized as follows.