HindiPersonalityNet: Personality Detection in Hindi Conversational Data using Deep Learning with Static Embedding

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Kumar, Akshi; Jain, Dipika and Beniwal, Rohit. 2024. HindiPersonalityNet: Personality Detection in Hindi Conversational Data using Deep Learning with Static Embedding. ACM Transactions on Asian and Low-Resource Language Information Processing, 23(8), 117. ISSN 2375-4699 [Article]

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Official URL: https://dl.acm.org/doi/10.1145/3625228

Abstract or Description

Personality detection along with other behavioural and cognitive assessment can essentially explain why people act the way they do and can be useful to various online applications such as recommender systems, job screening, matchmaking, and counselling. Additionally, psychometric NLP relying on textual cues and distinctive markers in writing style within conversational utterances reveal signs of individual personalities. This work demonstrates a text-based deep neural model, HindiPersonalityNet of classifying conversations into three personality categories {ambivert, extrovert, introvert} for detecting personality in Hindi conversational data. The model utilizes GRU with BioWordVec embeddings for text classification and is trained/tested on a novel dataset, शख्सियत (pronounced as Shakhsiyat) curated using dialogues from an Indian crime-thriller drama series, Aarya. The model achieves an F1-score of 0.701 and shows the potential for leveraging conversational data from various sources to understand and predict a person's personality traits. It exhibits the ability to capture semantic as well as long-distance dependencies in conversations and establishes the effectiveness of our dataset as a benchmark for personality detection in Hindi dialogue data. Further, a comprehensive comparison of various static and dynamic word embedding is done on our standardized dataset to ascertain the most suitable embedding method for personality detection.

Item Type:

Article

Identification Number (DOI):

https://doi.org/10.1145/3625228

Additional Information:

"© 2023 Copyright held by the owner/author(s). This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record is available at, https://doi.org/10.1145/3625228."

Keywords:

Personality, low-resource, deep learning, word embeddings, NLP

Departments, Centres and Research Units:

Computing

Dates: