Although backdoor learning is an active research topic in the NLP domain, the literature lacks studies that systematically categorize and summarize backdoor attacks and defenses. To bridge the gap, we present a comprehensive and unifying study of backdoor learning for NLP by summarizing the literature in a systematic manner. We first present and motivate the importance of backdoor learning for building robust NLP systems. Next, we provide a thorough account of backdoor attack techniques, their applications, defenses against backdoor attacks, and various mitigation techniques to remove backdoor attacks. We then provide a detailed review and analysis of evaluation metrics, benchmark datasets, threat models, and challenges related to backdoor learning in NLP. Ultimately, our work aims to crystallize and contextualize the landscape of existing literature in backdoor learning for the text domain and motivate further research in the field. To this end, we identify troubling gaps in the literature and offer insights and ideas into open challenges and future research directions. Finally, we provide a GitHub repository with a list of backdoor learning papers that will be continuously updated at https://github.com/marwanomar1/Backdoor-Learning-for-NLP.