In Japanese–English machine-translation,especially for creative and localized content, standard MT metrics typically summarize translation quality as scalar scores, but provide little diagnostic information about how style changes in translation. This is limiting because politeness, directness, interpersonal stance, and character voice are encoded differently across Japanese and English, and stylistic change may reflect localization-oriented adaptation rather than error. We propose the Style Diagnostic Profile (SDP) framework: SDP assigns each utterance a seven-dimensional bipolar style vector, and 𝛥sdp, the difference between target and source SDP vectors, represents translation-induced stylistic change as a multi-axis shift vector. To evaluate the framework, we first validate an LLM-based SDP scorer against bilingual human annotations on professional JA→EN subtitles and show that it approximates human style judgments. Using Style25, a controlled JA↔EN style-perturbation benchmark, we demonstrate that different perturbation categories occupy distinct regions in 𝛥sdp space. As a case study, we analyze professional subtitles and observe stylistic adaptation rather than simple style copying. These results position the SDP framework as a diagnostic layer complementary to adequacy-oriented MT evaluation.