Personality Assessment from Text for Machine Commonsense Reasoning

• URL: http://arxiv.org/abs/2004.09275v1
• Date: Wed, 15 Apr 2020 07:30:47 GMT
• Title: Personality Assessment from Text for Machine Commonsense Reasoning
• Authors: Niloofar Hezarjaribi, Zhila Esna Ashari, James F. Frenzel, Hassan Ghasemzadeh, and Saied Hemati
• Abstract summary: PerSense is a framework to estimate human personality traits based on expressed texts. Our goal is to demonstrate the feasibility of using machine learning algorithms on personality trait data.
• Score: 15.348792748868643
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This article presents PerSense, a framework to estimate human personality traits based on expressed texts and to use them for commonsense reasoning analysis. The personality assessment approaches include an aggregated Probability Density Functions (PDF), and Machine Learning (ML) models. Our goal is to demonstrate the feasibility of using machine learning algorithms on personality trait data to predict humans' responses to open-ended commonsense questions. We assess the performance of the PerSense algorithms for personality assessment by conducting an experiment focused on Neuroticism, an important personality trait crucial in mental health analysis and suicide prevention by collecting data from a diverse population with different Neuroticism scores. Our analysis shows that the algorithms achieve comparable results to the ground truth data. Specifically, the PDF approach achieves 97% accuracy when the confidence factor, the logarithmic ratio of the first to the second guess probability, is greater than 3. Additionally, ML approach obtains its highest accuracy, 82.2%, with a multilayer Perceptron classifier. To assess the feasibility of commonsense reasoning analysis, we train ML algorithms to predict responses to commonsense questions. Our analysis of data collected with 300 participants demonstrate that PerSense predicts answers to commonsense questions with 82.3% accuracy using a Random Forest classifier.

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