Personality Recognition from Text in Indian Languages : Challenges, Progress, and Future Directions
DOI:
https://doi.org/10.32628/CSEIT251117136Keywords:
Personality Recognition, Computational Linguistics, Indian Languages, Low-Resource NLP, Big Five Model, Code-Switching, Multimodal AI, Transfer LearningAbstract
Personality Recognition (PR) from text is an emerging area within computational linguistics that seeks to automatically infer an individual’s personality traits based on their written or spoken language. While substantial progress has been achieved for English and other high-resource languages, research on Indian languages remains at an early stage. This review presents a comprehensive synthesis of the current work on PR for Indian languages. We examine the linguistic, cultural, and technical challenges unique to this context such as morphological complexity, code-switching, data scarcity, and the cultural applicability of Western personality frameworks like the Big Five model. The paper systematically reviews available datasets, methodologies (ranging from traditional machine learning to state-of-the-art large language models), and evaluation strategies. Our analysis highlights a clear performance gap between English-based systems and those developed for Indian languages, primarily driven by limited resources and cultural differences in language use. We conclude by identifying key research gaps and proposing a roadmap for the future emphasizing the creation of large, diverse, and multimodal datasets; the adaptation of culturally relevant personality models; and the fine-tuning of large language models for India’s multilingual environment.
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