An Effective Personality Recognition Model Design using Generative Artificial Intelligence based Learning Principles

Jenifa, G.; K, Padmapriya.; P, Sevanthi; Karthika, K.; Pandi, V. Samuthira; Arumugam, Deepak

doi:10.1109/iccds60734.2024.10560368

2024 International Conference on Computing and Data Science (ICCDS) 2024

DOI: 10.1109/iccds60734.2024.10560368

|View full text |Cite

An Effective Personality Recognition Model Design using Generative Artificial Intelligence based Learning Principles

G. Jenifa,

Padmapriya. K,

Sevanthi P

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Using deep learning and word embeddings for predicting human agreeableness behavior

Alsini,

Naz,

Khan

et al. 2024

Sci Rep

View full text Add to dashboard Cite

The latest advancements of deep learning have resulted in a new era of natural language processing. The machines now possess an unparallel ability to interpret and engage with various tasks such as text classification, content generation and natural language understanding. This development extended to the analysis of human behavior, where deep learning models are used to decode human personality. Due to the rise of social media, generating huge amounts of textual data that reshaped communication patterns. Understanding personality traits is a challenging topic which helps us to explore the patterns of thoughts, feelings and behaviors which are helpful for recruitment, career counselling and consumers’ behavior for marketing, etc. In this research study, the main aim is to predict the human personality trait of agreeableness showing whether a person is emotional who feels a lot or thinker who is logical and has rational thinking. This behavior leads to analyzing them as cooperative, friendly and respecting difference of views. For comprehensive empirical analysis, shallow machine learning models, ensemble models, and deep learning technique including state of the art transformer-based models are applied on real-world dataset of MBTI. For feature engineering, textual features of TF-IDF and POS tagging and word embeddings such as word2vec, glove and sentence embeddings are explored. The results analysis shows the highest performance 91.57% with sentence embeddings utilizing Bi-LSTM algorithm that highlights the power of this study as compared to existing studies in the relevant literature.

show abstract

Using deep learning and word embeddings for predicting human agreeableness behavior

Alsini,

Naz,

Khan

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

An Effective Personality Recognition Model Design using Generative Artificial Intelligence based Learning Principles

Cited by 1 publication

References 10 publications

Using deep learning and word embeddings for predicting human agreeableness behavior

Using deep learning and word embeddings for predicting human agreeableness behavior

Contact Info

Product

Resources

About