Abstract
Intelligent agents have the potential to understand personality traits of human beings because of their every day interaction with us. The assessment of our psychological traits is a useful tool when we require them to simulate empathy. Since the creation of social media platforms, numerous studies dealt with measuring personality traits by gathering users’ information from their social media profiles. Real world applications showed how natural language processing combined with supervised machine learning algorithms are effective in this field. These applications have some limitations such as focusing on English text only and not considering polysemy in text. In this paper, we propose a multilingual model that handles polysemy by analyzing sentences as a semantic ensemble of interconnected words. The proposed approach processes Facebook posts from the myPersonality dataset and it turns them into a high-dimensional array of features, which are then exploited by a deep neural network architecture based on transformer to perform regression. We prove the effectiveness of our work by comparing the mean squared error of our model with existing baselines and the Kullback–Leibler divergence between the relative data distributions. We obtained state-of-the-art results in personality traits estimation from social media posts for all five personality traits.
Highlights
Language models have been widely employed to measure personality traits starting from written text
We prove the effectiveness of our work by comparing the mean squared error of our model with existing baselines and the Kullback–Leibler divergence between the relative data distributions
To assess that our model is improving on the actual state of the art, we compared it with previous models as well as different configurations of the encoding and regression stages
Summary
Language models have been widely employed to measure personality traits starting from written text. A Japanese airline company improved flight experiences by empowering AI with personality trait assessment skills in their customer communication chatbot (https://www.ibm.com/blogs/client-voices/ai-personalizes-japan-airlinestravel-experience/). These works show how the ability to recognize the semantic meaning of human language led to a fine personality trait measurement. They detected social risks and they improved user experience. Even if these solutions have a big impact on society, we argue that current techniques do not consider polysemy in text and differences among languages.
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