ABSTRACT
Text occupies a foundational role in social inquiry, serving as a primary medium through which social meanings, attitudes, and narratives are expressed and analyzed. This chapter presents a comprehensive natural language processing (NLP) workflow that includes preprocessing, named entity recognition, sentiment and stance analysis, topic modeling, and embedding techniques. It also examines transformer-based models that enable scalable “distant reading” of large textual corpora while maintaining analytical depth through complementary “close reading” strategies. The chapter further explores AI-assisted qualitative analysis, encompassing automated coding, thematic clustering, and retrieval of representative quotations while emphasizing the importance of human-in-the-loop validation to preserve contextual accuracy. Particular attention is devoted to challenges such as sarcasm detection, dialectal variation, and multilingual interpretation. A practical component addresses the implementation of explainable text analysis models, the maintenance of transparent audit trails documenting coding modifications, and the alignment of algorithmically generated themes with established theoretical frameworks to prevent superficial categorization. The proposed workflow offers a replicable and efficient analytical pipeline that accelerates coding processes, uncovers latent discursive patterns, and reinforces the central role of researcher interpretation in producing meaningful social insights.
