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Machine Learning Approach to Improve Data Connectivity in Text-based Personality Prediction using Multiple Data Sources Mapping

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Title Machine Learning Approach to Improve Data Connectivity in Text-based Personality Prediction using Multiple Data Sources Mapping
 
Creator Johnson, Sirasapalli Joshua
Murty, M Ramakrishna
 
Subject BERT
Deep learning
Natural language processing
Personality detection
Social media
 
Description 109-119
This paper considers the task of personality prediction using social media text data. Personality datasets with
conventional personality labels are few, and collecting them is challenging due to privacy concerns and the high expense of
hiring expert psychologists to label them. Pertaining to a smaller number of labelled samples available, existing studies
usually adds a sentiment, statistical NLP features to the text data to improve the accuracy of the personality detection model.
To overcome these concerns, this research proposes a new methodology to generate a large amount of labelled data that can
be used by deep learning algorithms. The model has three components: general data representation, data mapping and
classification. The model applies Personality correlation descriptors to incorporate correlation information and further use
this information in generating dataset mapping algorithm. Experimental results clearly demonstrate that the proposed
method beats strong baselines across a variety of evaluation metrics. The results had the highest accuracy of 86.24% and
0.915 F1 measure score on the combined MBTI and Essays dataset. Moreover, the new dataset constructed contains
3,84,089 labelled samples on the combined dataset and can be further considered for personality prediction using the famous
Five Factor Model thereby alleviating the problem of limited labelled samples for the purpose of personality detection.
 
Date 2023-01-16T10:26:43Z
2023-01-16T10:26:43Z
2023-01
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/61199
https://doi.org/10.56042/jsir.v82i1.70218
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source JSIR Vol.82(01) [January 2023]