| Sumario: | Text‐based automatic personality recognition (APR) operates at the intersection of artificial intelligence (AI) and psychology to determine the personality of an individual from their text sample. This covert form of personality assessment is key for a variety of online applications that contribute to individual convenience and well‐being such as that of chatbots and personal assistants. Despite the availability of good quality data utilizing state‐of‐the‐art AI methods, the reported performance of these recognition systems remains below expectations in comparable areas. Consequently, this work investigates and identifies the source of this performance limit and attributes it to the flawed assumptions of text‐based APR. These insights are obtained via a large‐scale comprehensive benchmark and analysis of text data from five corpora with diverse characteristics and complementary personality models (Big Five and Dark Triad) applied to an assortment of AI methods ranging from hand‐crafted linguistic features to data‐driven transformers. Finally, the work concludes by identifying the open problems that can help navigate the limitations in text‐based automatic personality recognition to a great extent.
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