The linguistic category model (LCM) seeks to understand social psychological processes through the lens of language use. Its original development required human judges to analyze natural language to understand how people assess actions, states, and traits. The current project sought to computerize the LCM assessment based on an idea of language abstraction with a previously published data set. In the study, a computerized LCM analysis method was built using an LCM verb dictionary and a part-of-speech tagging program that identified relevant adjectives and nouns. This computerized method compared open-ended texts written in first-person and third-person perspectives from 130 college students. Consistent with construal-level theory, third-person writing resulted in higher levels of abstraction than first-person writing. Implications of relying on an automated LCM method are discussed.