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Using Stylometric Features to Predict Author Personality Type in Modern Greek Essays

Authors:
Sofia Gagiatsou
Georgios Markopoulos
George Mikros

Keywords: Author profiling; stylometry; Personality prediction; Jung Typology Test; corpus processing; computational stylistics; machine learning.

Abstract:
We present a research focused on the prediction of the author's personality based on natural language processing techniques applied to essays written in Modern Greek by high-school students. Each writer has been profiled by filling in the Jung Typology Test. In addition, personality prediction is being discussed under the general research framework of author profiling by examining the effectiveness of several stylometric features to predict students’ personality types. The feature set we employed was a combination of the word and sentence length, the most frequent part-of-speech tags, most frequent character/word bigrams and trigrams, most frequent words, as well as hapax/dis legomena. Since personality prediction represents a complex multidimensional research problem, we applied various machine learning algorithms to optimize our model’s performance after extracting the stylometric features. We compared nine machine learning algorithms and ranked them according to their cross-validated accuracy. The best results were obtained by the Naive Bayes algorithm. According to the personality classification based on the Jung Typology Test, the author’s personality prediction accuracy reached 80.7% on Extraversion, 79.9% on Intuition, 68.8% on Feeling, 75.7% on Judging, according to the personality classification. The reported results show a competitive approach to the personality prediction problem. Furthermore, our research revealed new combinations of stylometric features and corresponding computational techniques, giving interesting and satisfying solutions to the problem of the author’s personality prediction for Modern Greek.

Pages: 34 to 39

Copyright: Copyright (c) IARIA, 2021

Publication date: July 18, 2021

Published in: conference

ISSN: 2308-3956

ISBN: 978-1-61208-869-3

Location: Nice, France

Dates: from July 18, 2021 to July 22, 2021