Last week I bumped into Francois Mairesse’s open source Personality recogniser, of which he has a web demo where you can input emails, essays, chat logs, thoughts or other texts produced by you and get your personality scores for all Big Five traits (Extraversion, Emotional stability, Conscientiousness and Openness to experience) as well as the model used to compute them. Statistical models which can be used are SVM with Linear Kernel, M5 Model Tree, M5 Regression Tree and Linear Regression, the Support Vector Machine (SVM) being the most general one.
Obviously, the more text you feed the recogniser the more accurate it gets.
How this works is quite interesting. Here’s a summary of how markers are grouped (Mairesse, et. al., 2007):
- standard counts (e.g. words count, word per sentence, syllables per words, frequency of use, words longer than 6 letters, negations, articles, pronouns)
- psychology processes (e.g. positive & negative emotions, causation, tentative, references to people)
- relativity (e.g. past tense verb, future tense verb, up, down, inclusive, exclusive, motion)
- personal concerns (e.g. school, work, achievements, TV, movies, music, moeny, religion, death, sexuality, eating, sleeping)
- utterance (e.g. ratio of commands, questions, assertions)
- other dimensions (e.g. ponctuation, swear words, fillers, familiarity rating, meaning fullness Paivio norm, different Kucera-Francis frequencies)
