Human personality is significantly represented by those words which he/she uses in his/her speech or writing. As a consequence of spreading the information infrastructures (specifically the Internet and social media), human communications have reformed notably from face to face communication. Generally, Automatic Personality Prediction (or Perception) (APP) is the automated forecasting of the personality on different types of human generated/exchanged contents (like text, speech, image, video, etc.). The major objective of this study is to enhance the accuracy of APP from the text. To this end, we suggest five new APP methods including term frequency vector-based, ontology-based, enriched ontologybased, latent semantic analysis (LSA)-based, and deep learning-based (BiLSTM) methods. These methods as the base ones, contribute to each other to enhance the APP accuracy through ensemble modeling (stacking) based on a hierarchical attention network (HAN) as the meta-model. The results show that ensemble modeling enhances the accuracy of APP.
Eser Adı (dc.title) | Automatic personality prediction: an enhanced method using ensemble modeling |
Yazar (dc.contributor.author) | Taymaz Akan |
Yayın Yılı (dc.date.issued) | 2022 |
Tür (dc.type) | Makale |
Özet (dc.description.abstract) | Human personality is significantly represented by those words which he/she uses in his/her speech or writing. As a consequence of spreading the information infrastructures (specifically the Internet and social media), human communications have reformed notably from face to face communication. Generally, Automatic Personality Prediction (or Perception) (APP) is the automated forecasting of the personality on different types of human generated/exchanged contents (like text, speech, image, video, etc.). The major objective of this study is to enhance the accuracy of APP from the text. To this end, we suggest five new APP methods including term frequency vector-based, ontology-based, enriched ontologybased, latent semantic analysis (LSA)-based, and deep learning-based (BiLSTM) methods. These methods as the base ones, contribute to each other to enhance the APP accuracy through ensemble modeling (stacking) based on a hierarchical attention network (HAN) as the meta-model. The results show that ensemble modeling enhances the accuracy of APP. |
Açık Erişim Tarihi (dc.date.available) | 2022-06-15 |
Yayıncı (dc.publisher) | ORIGINAL ARTICLE |
Dil (dc.language.iso) | En |
Konu Başlıkları (dc.subject) | Automatic personality prediction (APP) Natural language processing Ensemble modeling Big five model |
Konu Başlıkları (dc.subject) | Automatic personality prediction (APP) |
Konu Başlıkları (dc.subject) | Natural language processing |
Konu Başlıkları (dc.subject) | Ensemble modeling Big five model |
Tek Biçim Adres (dc.identifier.uri) | https://hdl.handle.net/20.500.14081/1563 |
ISSN (dc.identifier.issn) | 0941-0643 |
Dergi (dc.relation.journal) | Neural Computing & Applications |
Dergi Sayısı (dc.identifier.issue) | 21 |
Esere Katkı Sağlayan (dc.contributor.other) | Akan, Taymaz |
Esere Katkı Sağlayan (dc.contributor.other) | Alp, Sait |
Esere Katkı Sağlayan (dc.contributor.other) | Bhuiyan, Mohammad A. N. |
Esere Katkı Sağlayan (dc.contributor.other) | Dehkharghani, Rahim |
DOI (dc.identifier.doi) | 10.1007/s00521-022-07444-6 |
Orcid (dc.identifier.orcid) | 0000-0003-4070-1058 |
Bitiş Sayfası (dc.identifier.endpage) | 18389 |
Başlangıç Sayfası (dc.identifier.startpage) | 18369 |
Dergi Cilt (dc.identifier.volume) | 34 |
wosquality (dc.identifier.wosquality) | Q2 |
wosauthorid (dc.contributor.wosauthorid) | S-4564-2019 |
Department (dc.contributor.department) | Yazılım Mühendisliği |
Wos No (dc.identifier.wos) | WOS:001084223600005 |
Veritabanları (dc.source.platform) | Wos |
Veritabanları (dc.source.platform) | Scopus |