In this study, we analyze spontaneous speech transcripts from Hungarian patients with schizophrenia, schizoaffective, and bipolar disorders. Our goal is to identify distinctive linguistic features in these patient groups and controls. To our knowledge, no prior study has systematically examined the linguistic features of these disorders or explored their use in distinguishing between these patient groups. We collected recordings from 77 participants during three directed spontaneous speech tasks in a clinical setting, resulting in 458 texts. Our research group manually transcribed the recordings. We processed the written corpus texts using Natural Language Processing methods and tools. The final corpus consists of 179,515 tokens, excluding punctuation. Using this data, we analyze different linguistic features’ predictive power by computing and comparing their frequency distributions. We then attempt to automatically differentiate between patient groups and controls using our extensive set of linguistic features, employing the random forest algorithm in these experiments. Our results indicate that applying machine learning techniques based on distinctive features can effectively distinguish SZ, SAD, BD, and controls, surpassing baseline results.