Predicting Consumer Personalities from What They Say

Abstract

This study mapped personality based on the newly proposed extraction method from consumers’ textual data and revealed the relevance (attention) and polarity (affection) of words associated with a specific personality trait. Furthermore, we illustrate how unique words are used to predict a consumer’s behavior associated with certain personality traits. In this study, we employed the scales of the Kaggle MBTI Personality dataset to examine the methodology’s effectiveness, extract the personality traits from the textual data into features, and map them into the traits/dimensions of the existing scale. Based on the results obtained in this study, we assert that using the TF-IDF algorithm is a good way to generate a custom dictionary. Furthermore, sentiment scoring with an AI-empowered machine learning algorithm provides useful data to filter and validate more coherent words to understand and, thus, communicate a particular aspect of personality. Finally, we proposed that four situations involving the interaction between attention (frequency) and affection (sentiment) allow us to better understand the consumer and how to use the feature words in terms of the interaction between attention (TF-IDF score) and affection (sentiment score).

A Study on MBTI Perceptions in South Korea: Big Data Analysis from the Perspective of Applying MBTI to Contribute to the Sustainable Growth of Communities

“…These studies have primarily concentrated on the utility and scientific validation of the MBTI. In recent years, interdisciplinary approaches have emerged, including the use of machine learning to predict MBTI types [36][37][38] and employing computational linguistics to examine the correlation between voice characteristics and MBTI types [39].…”