Evading text based emotion detection mechanism via adversarial attacks

Abstract

Textual Emotion Analysis (TEA) seeks to extract and assess the emotional states of users from the text. Various Deep Learning (DL) algorithms have emerged rapidly and demonstrated success in numerous disciplines, including audio, image, and natural language processing. Thetrend has shifted a growing number of researchers from standard machine learning to DL for scientific study. Using DL approaches, we offer an overview of TEA in this paper. After introducing the background for emotion analysis, including the definition of emotion, emotion classification methods, and application domains of emotion analysis, we demonstrated that, despite the immense success of deep learning models in NLP-related tasks, they are susceptible to adversarial attacks, which can lead to incorrect emotion classification. An adversarial text is constructed by altering a few words or characters so as to keep the overall semantic similarity of emotion for a human reader while tricking the machine into making erroneous predictions. This study demonstrates the vulnerability of emotion categorization by generating adversarial text using a variety of cutting-edge attack techniques. Comprehensive experiments are performed to assess the effectiveness of the attack methods against several widely-used models, such as Word-CNN, Bi-LSTM, and four powerful transformer models, namely BERT, DistilBERT, ALBERT, and RoBERTa. These models were trained on an emotion dataset utilized for the purpose of emotion classification. We evaluated and analyzed the behavior of different models under a variety of attack conditions to determine which is the most and least vulnerable. Also, we determine which perturbation technique affects transformer models the most. Using Attack Success Rates (ASR) as our evaluation metric, we have assessed the potential outcomes. The findings reveal that methodologies for classifying emotion predictioncan be circumvented, which has implications for existing policy measures.