Abstract The objective of the article is twofold: first, to employ the knowledge of the recurrence of low-frequency words in authorial texts; and second, to prevent the misuse of this knowledge. Contrary to the prevailing authorship attribution theory and practice (Evert et al. 2017, Juola 2008), our research has revealed that the personal linguistic profile is not primarily composed of frequent words with grammatical functions. Instead, we have identified that a distinct set of full-meaning words defines an individual’s linguistic profile (Faltýnek 2020, Faltýnek – Matlach 2021). An examination of these meanings reveals an individual’s unconscious language habits and, consequently, their personality settings. Such personal profiling is referred to as “deep content” and “deep sentiment analysis”. The innovation in question has the potential to facilitate a novel form of linguistic personalization in digital communication, one that has not been previously observed or utilized. The main aim of this article is to describe the algorithm to conduct single-person linguistic deep content and deep sentiment profiling and personalization. We will describe technical steps to provide such a form of digital communication processing and to facilitate the adjustment of a text targeted at an individual, described as a System and method for adapting text-based data structures to text samples (Patent No.: US11797753B2, Faltýnek et al. 2023). This algorithm can be used to (a) produce a personal linguistic profile (analogically to psychometrics instruments such as NEO-FFI Big Five, Minnesota Multiphasic Personality Inventory (MMPI)), (b) target digital communication to an individual by “translating” a text to their language (i.e. linguistic habits) and stimulate desired feelings to a predetermined content. The algorithm is, however, also designed (c) to be used to avoid procedures (a) and (b) using any kind of digital communication platform by an individual. This algorithm is implemented in the software Cloakspeech (Faltýnek – Benešová – Kučera 2025), which provides personalization of AI-generated texts: AI speaks like a particular person.
Faltýnek et al. (2025) studied this question.