HindiPersonalityNet: Personality Detection in Hindi Conversational Data Using Deep Learning with Static Embedding
Article No.: 117, Pages 1 - 13
Abstract
Personality detection along with other behavioral 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 counseling. Additionally, psychometric natural language processing relying on textual cues and distinctive markers in writing style within conversational utterances reveals 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 a gated recurrent unit 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 both semantic and 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.
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Index Terms
- HindiPersonalityNet: Personality Detection in Hindi Conversational Data Using Deep Learning with Static Embedding
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Published In
August 2024
343 pages
EISSN:2375-4702
DOI:10.1145/3613611
- Editor:
- Imed Zitouni,
- Guest Editors:
- Deepak Kumar Jain,
- Thierry Boumans,
- Stefano Berretti
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 07 August 2024
Online AM: 29 September 2023
Accepted: 16 September 2023
Revised: 14 August 2023
Received: 24 May 2023
Published in TALLIP Volume 23, Issue 8
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