Jain, Dipika and Kumar, Akshi. 2024. AI Unveiled Personalities: Profiling Optimistic and Pessimistic Attitudes in Hindi Dataset using Transformer-based Models. Experts System, ISSN 0266-4720 [Article] (In Press)

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Abstract or Description

Both optimism and pessimism are intricately intertwined with an individual's inherent personality traits and people of all personality types can exhibit a wide range of attitudes and behaviours, including levels of optimism and pessimism. This paper undertakes a comprehensive analysis of optimistic and pessimistic tendencies present within Hindi textual data, employing transformer-based models. The research represents a pioneering effort to define and establish an interaction between the personality and attitude chakras within the realm of human psychology. Introducing an innovative "Chakra" system to illustrate complex interrelationships within human psychology, this work aligns the Myers-Briggs Type Indicator (MBTI) personality traits with optimistic and pessimistic attitudes, enriching our understanding of emotional projection in text. The study employs meticulously fine-tuned transformer models—specifically mBERT, XLM-RoBERTa, IndicBERT, mDeBERTa and a novel stacked mDeBERTa —trained on the novel Hindi dataset ‘मनोभाव’ (pronounced as Manobhav). Remarkably, the proposed Stacked mDeBERTa model outperforms others, recording an accuracy of 0.7785 along with elevated precision, recall, and F1 score values. Notably, its ROC AUC score of 0.7226 underlines its robustness in distinguishing between positive and negative emotional attitudes. The comparative analysis highlights the superiority of the Stacked mDeBERTa model in effectively capturing emotional attitudes in Hindi text.

Item Type:	Article
Identification Number (DOI):	https://doi.org/10.1111/exsy.13572
Additional Information:	The data that support the findings of this study are available from the corresponding author upon reasonable request.
Keywords:	optimism, pessimism, MBTI, machine learning, deep learning, transformers
Departments, Centres and Research Units:	Computing
Dates:	Date Event 17 February 2024 Accepted 12 March 2024 Published
Item ID:	35738
Date Deposited:	26 Mar 2024 13:11
Last Modified:	24 Apr 2024 10:06
Peer Reviewed:	Yes, this version has been peer-reviewed.
URI:	https://research.gold.ac.uk/id/eprint/35738

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