Abstract: In this study we examined the possibility to extract personality traits from a text. We created an extensive dataset by having experts annotate personality traits in a large number of texts from multiple online sources. From these annotated texts we selected a sample and made further annotations ending up with a large low-reliability dataset and a small high-reliability dataset. We then used the two datasets to train and test several machine learning models to extract personality from text, including a language model. Finally, we evaluated our best models in the wild, on datasets from different domains. Our results show that the models based on the small high-reliability dataset performed better (in terms of R2) than models based on large low-reliability dataset. Also, the language model based on the small high-reliability dataset performed better than the random baseline. Finally, and more importantly, the results showed our best model did not perform better than the random baseline when tested in the wild. Taken together, our results show that determining personality traits from a text remains a challenge and that no firm conclusions can be made on model performance before testing in the wild.

Introduction: Since the introduction of the personality concept, psychologists have worked to formulate theories and create models describing human personality and reliable measure to accordingly. The filed has been successful to bring forth a number of robust models with corresponding measures. One of the most widely accepted and used is the Five Factor Model BIBREF0. The model describes human personality by five traits/factors, popularly referred to as the Big Five or OCEAN: Openness to experience, Conscientiousness, Extraversion, Agreeableness, and emotional stability (henceforth Stability). There is now an extensive body of research showing that these factors matter in a large number of domains of people’s life. Specifically, the Big Five factors have been found to predict life outcomes such as health, longevity, work performance, interpersonal relations, migration and social attitudes, just to mention some domains (e.g. BIBREF1, BIBREF2, BIBREF3, BIBREF4).To date, the most common assessment of personality is by self-report questionnaires BIBREF5. In the past decade however, personality psychologist, together with computer scientist, have worked hard to solve the puzzle of extracting a personality profile (e.g., the Big Five factors) of an individual based on a combination of social media activities BIBREF6. However, in the aftermath of Cambridge Analytica scandal, where the privacy of millions of Facebook users was violated, this line of research has been met with skepticism and suspicion. More recent research focuses on text from a variety of sources, including twitter data (e.g. BIBREF7, BIBREF8). Recent development in text analysis, machine learning, and natural language models, have move the field into an era of optimism, like never before. Importantly, the basic idea in this research is that personality is reflected in the way people write and that written communication includes information about the author’s personality characteristics BIBREF9. Nevertheless, while a number of attempts has been made to extract personality from text (see below), the research is standing remarkably far from reality. There are, to our knowledge, very few attempts to test machine learning models “in the wild”. The present paper aims to deal with this concern. Specifically, we aim to (A) create a model which is able to extract Big Five personality from a text using machine learning techniques, (B) investigate whether a model trained on a large amount of solo-annotated data performs better than a model trained on a smaller amount of high quality data, and, (C) measure the performance of our models on data from another two domains that differ from the training data.

Question: What is the agreement of the dataset?
Answer: Answer with content missing: (Table 2): Krippednorff's alpha coefficient for dataset is: Stability -0.26, Extraversion 0.07, Openness 0.36, Agreeableness 0.51, Conscientiousness 0.31