# A Survey of Adversarial Attacks: An Open Issue for Deep Learning Sentiment Analysis Models
###### Abstract  
In recent years, the use of deep learning models for deploying sentiment analysis systems has become a widespread topic due to their processing capacity and superior results on large volumes of information. However, after several years' research, previous works have demonstrated that deep learning models are vulnerable to strategically modified inputs called _adversarial examples_. Adversarial examples are generated by performing perturbations on data input that are imperceptible to humans but that can fool deep learning models' understanding of the inputs and lead to false predictions being generated. In this work, we collect, select, summarize, discuss, and comprehensively analyze research works to generate textual adversarial examples. There are already a number of reviews in the existing literature concerning attacks on deep learning models for text applications; in contrast to previous works, however, we review works mainly oriented to sentiment analysis tasks. Further, we cover the related information concerning generation of adversarial examples to make this work self-contained. Finally, we draw on the reviewed literature to discuss adversarial example design in the context of sentiment analysis tasks. adversarial attacks; sentiment analysis; deep learning; vulnerabilities +
Footnote †: journal: Medicalopinions [3]. The purpose of sentiment analysis systems is to provide summarized information and assist potential users and organizations in evaluating their selections; popular sentiment analysis applications include social media monitoring, customer support management, and analyzing customer feedback, among others. Sentiment analysis has been predominantly applied to text analysis (analysis of user opinions). However, SA can be applied to different modalities as well, such as visual, speech, and text [4]. In recent years, multimodal sentiment analysis models have been proposed that combine different modalities to obtain more accurate results [5]. In this paper, we focus on sentiment analysis considering text modality. In recent years, deep learning (DL) models have become a popular research topic for better handling large volumes of information on sentiment analysis [6]. Deep learning models are potent algorithms that achieve excellent performance, allowing previous results to be outperformed in different areas. In sentiment analysis systems, the aim of implementing deep learning models is to improve the precision of results obtained with traditional machine learning algorithms and raise the confidence of users; however, this does not always turn out to be true. Figure 1 presents the general operation of a sentiment analysis model. After several years of research, it has been demonstrated both in theory and practice that DL models can be fooled with high probability by small modifications to the input data, causing incorrect results [7; 8]. Research such as [7; 8] has evaluated the robustness of deep learning models in face of small changes to the input data. [PERSON] et al. [7] evaluated state-of-the-art deep neuronal network (DNN) models for image classification by making minor pixel-level modifications to inputs, identifying that DNN models can be fooled with a high probability by modified inputs causing classification errors even though people could not identify any change. The resulting images obtained after performing modifications called _adversarial examples_. Subsequently, this name has been applied to denote intentional modifications made to input data to fool models. Based on the idea of adversarial examples, [PERSON] and [PERSON] [9] were the first to consider designing adversarial examples to fool DNN models for text applications (named textual deep neuronal networks). They generated short texts using words included in the training set of the neuronal network model.