This chapter introduces computational approaches to natural language argumentation, serving as a foundation for the pathos mining methods presented in subsequent chapters. It outlines the two main strands of computational argumentation: formal argumentation frameworks and natural language argument mining. The chapter focuses on the latter, reviewing tools, corpora, and techniques used to model and extract argumentative structures from texts. It describes the progression from lexical methods through supervised machine learning to transformer-based and large language models (LLMs), showing how each has contributed to automating argument mining. The chapter presents recent developments in the use of LLMs for classification tasks, annotation support, and persuasive discourse analysis, while critically evaluating claims about intentionality and strategy in AI-generated argumentation. It argues that although LLMs can emulate human-like argumentative behavior, their outputs remain grounded in statistical pattern matching. Finally, it identifies how LLMs and other computational tools can be used to model emotional appeals (pathos) in argumentation, in preparation for the pathos mining framework developed in later chapters.