{"pk":27107,"title":"Grammar-Based and Lexicon-Based Techniques to Extract Personality Traitsfrom Text","subtitle":null,"abstract":"Language provides an important source of information to pre-dict human personality. However, most studies that have pre-dicted personality traits using computational linguistic meth-ods have focused on lexicon-based information. We investigateto what extent the performance of lexicon-based and grammar-based methods compare when predicting personality traits. Weanalyzed a corpus of student essays and their personality traitsusing two lexicon-based approaches, one top-down (Linguis-tic Inquiry and Word Count (LIWC)), one bottom-up (topicmodels) and one grammar-driven approach (Biber model), aswell as combinations of these models. Results showed thatthe performance of the models and their combinations demon-strated similar performance, showing that lexicon-based top-down models and bottom-up models do not differ, and neitherdo lexicon-based models and grammar-based models. More-over, combination of models did not improve performance.These findings suggest that predicting personality traits fromtext remains difficult, but that the performance from lexicon-based and grammar-based models are on par.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[{"word":"language; personality; traits; machine learning;computational linguistics; lexicon-based; grammar-based"}],"section":"Posters: Papers","is_remote":true,"remote_url":"https://escholarship.org/uc/item/40k0g5j5","frozenauthors":[{"first_name":"Maira","middle_name":"B.","last_name":"Carvalho","name_suffix":"","institution":"Tilburg University","department":""},{"first_name":"Max","middle_name":"M.","last_name":"Louwerse","name_suffix":"","institution":"Tilburg University","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"2017-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/27107/galley/16743/download/"}]}