Automatic personality recognition of authors using big five factor model

Automatic personality recognition aims to assign a personal profile to the author by measuring the Big Five personality factors automatically. The present paper focuses on an approach developed to recognize the personality of the author by evaluating their writings. The score for each of the Big-Five personality traits is computed programmatically. Based on the essays of the author as an input, parse trees for the sentences are constructed to identify negations. Later, stopwords are removed, the text is tokenized and stemming is performed. Only the required information is extracted by using the parts of speech tagging in English language and the stemmed data is compared to the terms in the dataset which are categorized according to the Big Five Factors of personality. Finally, the matched percentage of words is calculated and scaled proportionately to obtain results.