Emotion detection in text-based content, such as opinions, comments, and textual interactions, holds pivotal significance in enabling computers to comprehend human emotions. This symbiotic understanding between machines and human languages, powered by technological advancements like Natural Language Processing and artificial intelligence, has revolutionized the dynamics of human-computer interaction. The complexity of emotion detection, although challenging, has surged in importance across diverse domains, encompassing customer service, healthcare, and surveillance of social media interactions. Within the realm of text analysis, the quest for accurate emotion detection necessitates a profound exploration of cutting-edge methodologies. This pursuit is further intensified by the imperative to fortify models against adversarial attacks, a pressing concern in deep learning-based approaches. To address this critical challenge, this paper introduces a pioneering technique—adversarial transfer learning—specifically tailored for emotion classification in text analysis. By infusing adversarial training into the model architecture, the proposed approach emerges a solution that not only mitigates the vulnerabilities of existing methods but also fortifies the model against adversarial intrusions. In realizing the potential of the proposed approach, a diverse array of datasets is harnessed for comprehensive training. The empirical results vividly demonstrate the efficacy of this approach, showcasing its superior performance when compared to state-of-the-art methodologies. Notably, the suggested approach yields in advancements in classification accuracy. In particular, the deployment of the Adversarial transfer learning methodology has increased in accuracy of 17.35%. This study, therefore, encapsulates a dual achievement: the introduction of an innovative approach that leverages adversarial transfer learning for emotion classification, and the subsequent empirical validation of its unparalleled efficiency. The implications reverberate across multiple sectors, extending the horizons of accurate emotion detection and laying a foundation for the next stride in human-computer interaction and emotion analysis.

Social media usage has increased tremendously with the rise of the internet and it has evolved into the most powerful networking platform of the twenty-first century. However, a number of undesirable phenomena are associated with increased use of social networking, such as cyberbullying (CB), cybercrime, online abuse and online trolling. Especially for children and women, cyberbullying can have severe psychological and physical effects, even leading to self-harm or suicide. Because of its significant detrimental social impact, the detection of CB text or messages on social media has attracted more research work. To mitigate CB, we have proposed an automated cyberbullying detection model that detects and classifies cyberbullying content as either bullying or non-bullying (binary classification model), creating a more secure social media experience. The proposed model uses Natural Language Processing (NLP) techniques and Machine Learning (ML) approaches to assess cyberbullying contents. Our main goal is to assess different machine learning algorithms for their performance in cyberbullying detection based on a labelled dataset from Formspring [1]. Nine popular machine learning classifiers namely Bootstrap Aggregation or Bagging, Stochastic Gradient Descent (SGD), Random Forest (RF), Decision Tree (DT), Linear Support Vector Classifier (Linear SVC), Logistic Regression (LR), Adaptive Boosting (AdaBoost), Multinomial Naive Bayes (MNB) and K-Nearest Neighbour (KNN) are considered for the work. In addition, we have experimented with a feature extraction method namely CountVectorizer to obtain features that aid for better classification. The results show that the classification accuracy of AdaBoost classifier is 86.52% which is found better than all other machine learning algorithms used in this study. The proposed work demonstrates the effectiveness of machine learning algorithms in automatic cyberbullying detection as against the very intense and time-consuming approaches for the same problem, thereby by facilitating easy incorporation of an effective approach as tools across different platforms enabling people to use social media safely.