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We introduce a general framework for analyzing large-scale text-based data, combining the strengths of neural-network language processing and generative statistical modeling. Our methodology generates textual factors by (i) representing texts using vector word embedding, (ii) clustering words using locality-sensitive hashing, and (iii) identifying spanning vector clusters through topic modeling. Our data-driven approach captures complex linguistic structures while ensuring computational scalability and economic interpretability. We also discuss applications of textual factors in (i) prediction and inference, (ii) interpreting (non-text-based) models and variables, and (iii) constructing new text-based metrics and explanatory variables, with illustrations using topics in finance and economics such as macroeconomic forecasting and factor asset pricing.