Abstract:
"Perosnality type identification is beneficial to understand the associates. Especially to understand life partner in order to have a successful marriage life, select the most suitable candidates for a company, and understand and explore the self capacities are some of them.
Personality is combination of a person’s behavior, feelings, motivation, and thought patterns. Those characteristics take years to understand and identify in a person’s personality. Personality type identification system was proposed to speed up the process.
In the study, the author identified that existing systems have a gap in identifying personality using unstructured text. To provide speedy and accurate information, the author selected to use Natural Language Processing and Machine Learning techniques.
Therefore, the author used the Decission Tree algorithm, the K-Neigbour Algorithm, Support Vector Machine, Naive Bayes algorithm, Logistic Regression algorithm, Random Forest algorithm, XGBoost model, and the LightGBM model. Considering the data set analysis, algorithms’ accuracy and evaluation metrics, the author developed Voting classifier ensemble model. To improve the accuracy, user balanced the dataset. If the original dataset was balanced, the author will be able to implement a more accurate model."
