Predicting personality traits with semantic structures and LSTM-based neural networks

Date
2022-10
Authors
Kosan, Muhammed Ali
Karacan, Hacer
Ürgen, Burcu Ayşen
Advisor
Instructor
Source Title
Alexandria Engineering Journal
Print ISSN
11100168
Electronic ISSN
Publisher
Elsevier
Volume
61
Issue
10
Pages
8007 - 8025
Language
English
Type
Article
Journal Title
Journal ISSN
Volume Title
Abstract

There is a need to obtain more information about target audiences in many areas such as law enforcement agencies, institutions, human resources, and advertising agencies. In this context, in addition to the information provided by individuals, their personal characteristics are also important. In particular, the predictability of personality traits of individuals is seen as a major parameter in making decisions about individuals. Textual and media data in social media, where people produce the most data, can provide clues about people's personal lives, characteristics, and personalities. Each social media environment may contain different assets and structures. Therefore, it is important to make a structural analysis according to the social media platform. There is also a need for a labelled dataset to develop a model that can predict personality traits from social media data. In this study, first, a personality dataset was created which was retrieved from Twitter and labelled with IBM Personality Insight. Then the unstructured data were transformed into meaningful and processable data, LSTM-based prediction models were created with the structural analysis, and evaluations were made on both our dataset and PAN-2015-EN. © 2022 THE AUTHORS

Course
Other identifiers
Book Title
Keywords
FastText, LSTM, Personality dataset, Personality traits, Prediction, Preprocessing
Citation
Published Version (Please cite this version)