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EmoMBTI‑Net introduces EmoMBTI, a dataset created by mapping scraped Reddit emojis to Myers‑Briggs Type Indicator (MBTI) traits, enabling emoji-focused natural language processing. Large language models including FlanT5, BART, and PEGASUS learn context links between emojis and text and are fine-tuned after dataset construction. Transformer models such as RoBERTa and DeBERTa are fine-tuned for MBTI prediction via emoji mappings, with the fine-tuned BART model reaching 0.875 accuracy, improving over RoBERTa (0.82) and DeBERTa (0.84).",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/emombti-net-introducing-and-leveraging-a-novel-emoji-dataset-for-personality-profiling-with-large-language-models/149266/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/emombti-net-introducing-and-leveraging-a-novel-emoji-dataset-for-personality-profiling-with-large-language-models/149266.png","ImageObject",300,407,{"name":92,"@type":93},"Asher","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-08-27",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",22,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What is EmoMBTI‑Net in this research?","Question",{"text":113,"@type":114},"EmoMBTI‑Net is an approach that leverages a purpose-built emoji dataset to predict MBTI personality types using large language models and transformer-based methods.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How was the EmoMBTI emoji dataset constructed?",{"text":118,"@type":114},"EmoMBTI was built by mapping emojis to specific MBTI traits using diverse posts scraped from Reddit.",{"name":120,"@type":111,"acceptedAnswer":121},"Which model achieved the best MBTI prediction accuracy and what was the result?",{"text":122,"@type":114},"The fine-tuned BART model achieved the highest accuracy of 0.875, outperforming RoBERTa (0.82) and DeBERTa (0.84).","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},149266,1787797302,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},687197207639,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","Social Network Analysis and Mining (2024) 14:234  \n[https://doi.org/10.1007/s13278-024-01400-z](https://doi.org/10.1007/s13278-024-01400-z)  \nEmoMBTI‑Net: introducing and leveraging a novel emoji dataset for personality profiling with large language models  \nAkshi Kumar1 · Dipika Jain2  \nReceived: 19 July 2024 / Revised: 27 November 2024 / Accepted: 28 November 2024 © The Author(s) 2024  \nAbstract  \nEmojis, integral to digital communication, often encapsulate complex emotional layers that enhance text beyond mere words. This research leverages the expressive power of emojis to predict Myers-Briggs Type Indicator (MBTI) personalities, diverging from conventional text-based approaches. We developed a unique dataset, EmoMBTI, by mapping emojis to specific MBTI traits using diverse posts scraped from Reddit. This dataset enabled the integration of Natural Language Processing (NLP) techniques tailored for emoji analysis. Large Language Models (LLMs) such as FlanT5, BART, and PEGASUS were trained to generate contextual linkages between text and emojis, further correlating these emojis with MBTI personalities. Following the creation of this dataset, these LLMs were applied to understand the context conveyed by emojis and were subsequently fine-tuned. Additionally, transformer models like RoBERTa, DeBERTa, and BART were specifically fine-tuned to predict MBTI personalities based on emoji mappings from MBTI dataset posts. Our methodology significantly enhances the capability of personality assessments, with the fine-tuned BART model achieving an impressive accuracy of 0.875 in predicting MBTI types, which notably exceeds the performances of RoBERTa and DeBERTa, at 0.82 and 0.84 respectively. By leveraging the nuanced communication potential of emojis, this approach not only advances personality profiling techniques but also deepens insights into digital behaviour, highlighting the substantial impact of emotive icons in online interactions.  \nKeywords Sentiment analysis · Personality · MBTI · Emojis · LLM · Natural language understanding  \n1 Introduction  \nAutomatic personality detection utilizes machine learning algorithms and natural language processing (NLP) techniques to analyze digital communication, such as texts, social media posts, or emails, inferring individuals' personality traits based on established models like the Myers-Briggs Type Indicator (MBTI) (Keh and Cheng 2019 ; Hernández et al. 2018 ; Cui and Qi 2017 ; Hernandez and Scott 2017 ; Ismail et al. 2017 ; Ren et al. 2021 ; Jain et al. 2021 ; Kumar et al. 2023). By examining linguistic patterns, word choices, and communication styles, algorithms can categorize individuals into different personality types, such as Introversion (I) vs. Extraversion (E), Sensing (S) vs. Intuition (N),  \n* Akshi Kumar [Akshi.Kumar@gold.ac.uk](Akshi.Kumar@gold.ac.uk)  \n1 Department of Computing, Goldsmiths, University of London, London, UK  \n2 Department of Computer Science and Engineering, Delhi Technological University, New Delhi, India  \nThinking (T) vs. Feeling (F), and Judging (J) vs. Perceiving (P) . This process offers valuable insights into behaviour, enables personalized digital experiences, and contributes to psychological research by studying large-scale datasets. Leveraging the MBTI framework, automatic personality detection algorithms can decode the nuances of digital behaviour, providing actionable insights for personal development, relationship building, targeted marketing, and a deeper understanding of human psychology in the digital age.  \nUser-generated content on social media platforms has become a valuable resource for personality detection, as it encompasses more than just text. Beyond written posts and comments, user-generated content includes images, videos, likes, shares, and other interactions, providing a rich source of data for inferring individuals' personality traits. Thus, a multi-modal approach to personality detection, incorporating textual, visual, and emotive","cbCaitBKFKwdG385","https://ap.wps.com/l/cbCaitBKFKwdG385","pdf",1876323,14,"English","# Abstract\n# Introduction\n## Motivation and background: personality detection from digital communication\n## Role of emojis in expressive, multimodal personality inference\n## Research goal and overview of the proposed approach","[{\"question\":\"What is EmoMBTI‑Net in this research?\",\"answer\":\"EmoMBTI‑Net is an approach that leverages a purpose-built emoji dataset to predict MBTI personality types using large language models and transformer-based methods.\"},{\"question\":\"How was the EmoMBTI emoji dataset constructed?\",\"answer\":\"EmoMBTI was built by mapping emojis to specific MBTI traits using diverse posts scraped from Reddit.\"},{\"question\":\"Which model achieved the best MBTI prediction accuracy and what was the result?\",\"answer\":\"The fine-tuned BART model achieved the highest accuracy of 0.875, outperforming RoBERTa (0.82) and DeBERTa (0.84).\"}]","EmoMBTI‑Net - introducing and leveraging a novel emoji dataset for personality profiling with large language models | PDF",35]