Document image tampering poses a serious threat to information authenticity in security-critical documents. Existing document tampering detection methods mainly rely on RGB-domain and frequency-domain artifacts, following a paradigm similar to natural image forensics, and often overlook a key property of document images: dense and semantically rich textual content. In this work, we propose TSPG, a plug-and-play Textual Semantic Prior Guidance module for document image tampering detection. TSPG uses a pretrained language model with a lightweight token classification head to estimate token-level tampering-aware semantic scores from OCR-recognized text. It then projects these scores back to the image space through OCR bounding boxes to construct a spatially aligned Textual Semantic Prior (TSP) map. To inject aforementioned prior into existing detectors, we design a Gated Semantic Residual Modulation (GSRM) module that adaptively modulates multi-scale detector features through residual gated guidance. TSPG can be inserted into existing document tampering detectors without modifying their original backbone or decoder. Experiments on DocTamper with two representative baselines, DTD and FFDN, show that TSPG consistently improves both models, demonstrating the complementary value of language-derived textual semantics for document tampering localization.