# A Survey of Adversarial Attacks: An Open Issue for Deep Learning Sentiment Analysis Models
###### Abstract
Then, the texts were added to the end of the original input data to generate the adversarial examples (also denominated adversarial texts), and it was observed that adversarial examples could also fool textual deep neuronal networks. Figure 2 shows an adversarial example designed in [9]. understand their current vulnerabilities (and continuously explore new vulnerabilities) when facing adversarial examples. This can help to propose defenses that effectively guarantee confidence in deep learning models' results. Due to the importance of sentiment analysis in the decision-making process, we survey adversarial attacks on textual deep neuronal networks in this work, specifically focusing on sentiment analysis. In this work, we aim to summarize efforts that expose the vulnerabilities presented by deep neuronal sentiment analysis models when facing adversarial examples. Considering the importance of sentiment analysis and the impact of adversarial examples, there is a need for this kind of work to provide successive researchers with an overview of extant efforts. **Related surveys and differences with this survey. ** Reviews of adversarial examples attacking deep learning models for text applications already exist in the literature, such as [10; 12; 13; 14]; however, to the best of our knowledge, no comprehensive review has collected and summarized the efforts in this research direction while specifically focusing on sentiment analysis. This study attempts to cover this gap via three central questions: (1) How do adversarial attack methods fool sentiment analysis models? (2) What proposals exist to defend against these attacks? and (3) What are the open challenges and directions of text adversarial attacks within sentiment analysis? **Paper selection methodology. ** We performed a literature review on three primary data sources related to computer science and data security: IEEE, ScienceDirect, and SpringerLink. First, keywords related to the central topic of this paper, namely, adversarial examples, were defined: (Adversarial, Examples, and Attack). Subsequently, keywords to delimit the research to sentiment analysis and text applications were added: (Text, Sentiment Analysis (SA), and Sentiment). Through the set of keywords defined above (Adversarial, Examples, Attack, Text, SA, Sentiment), the following query string was integrated: (Adversarial Attacks in Sentiment Analysis, Adversarial Attacks in Text). Considering that the research topic for text applications has emerged only recently, we limited the search to papers published from 2017 to January of 2024. The executed query string was as follows: _(\"adversarial attack\" or \"adversarial texts\") and (\"text\" or \"sentiment\" or \"sentiment analysis\")_. The inclusion and exclusion criteria for filtering and selecting papers were formally defined as follows:  
* The inclusion criteria considered the following elements: 1. The paper's contributions focus on the design of adversarial attack or defense mechanisms, particularly for the sentiment analysis task. 2. Priority was given to papers published in the most recognized conferences on Natural Language Processing and Artificial Intelligence (ACL: Annual Meeting of the Association for Computational Linguistics; COLING: International Conference on Computational Linguistics; EMNLP: Empirical Methods in Natural Language Processing; IJCAI: International Joint Conference on Artificial Intelligence), although this was not a limitation. Figure 2: Adversarial example from [9]. The assembly model originally obtains the correct answer, but is fooled by the insertion of an adversarial text (in blue). * On the other hand, the following exclusion criteria were defined: 1. Papers unrelated to text applications. 2. The content and contributions of the papers were not related to the research subject, i.e., adversarial attacks and their application in sentiment analysis. 3. Papers unrelated to sentiment analysis with deep neural network approaches. 4. The method presented in the paper was largely derived from other work.