Personality Trait Detection via Transfer Learning
作者机构：Department of Information Technologies and SystemsUniversity of Castilla-La ManchaCiudad Real13071Spain Institute of Artificial IntelligenceSchool of Computer Science and InformaticsDe Montfort UniversityThe GatewayLeicesterLE19BHUK
出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)
年 卷 期：2024年第78卷第2期
页 面：1933-1956页
核心收录：
学科分类：0502[文学-外国语言文学] 050201[文学-英语语言文学] 05[文学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：This work has been partially supported by FEDER and the State Research Agency(AEI)of the Spanish Ministry of Economy and Competition under Grant SAFER:PID2019-104735RB-C42(AEI/FEDER,UE) the General Subdirection for Gambling Regulation of the Spanish ConsumptionMinistry under the Grant Detec-EMO:SUBV23/00010 the Project PLEC2021-007681 funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR
主 题：Personality trait detection pre-trained language model big five model transfer learning
摘 要：Personality recognition plays a pivotal role when developing user-centric solutions such as recommender systems or decision support systems across various domains,including education,e-commerce,or human ***-ditional machine learning techniques have been broadly employed for personality trait identification;nevertheless,the development of new technologies based on deep learning has led to new opportunities to improve their *** study focuses on the capabilities of pre-trained language models such as BERT,RoBERTa,ALBERT,ELECTRA,ERNIE,or XLNet,to deal with the task of personality *** models are able to capture structural features from textual content and comprehend a multitude of language facets and complex features such as hierarchical relationships or long-term *** makes them suitable to classify multi-label personality traits from reviews while mitigating computational *** focus of this approach centers on developing an architecture based on different layers able to capture the semantic context and structural features from ***,it is able to fine-tune the previous models using the MyPersonality dataset,which comprises 9,917 status updates contributed by 250 Facebook *** status updates are categorized according to the well-known Big Five personality model,setting the stage for a comprehensive exploration of personality *** test the proposal,a set of experiments have been performed using different metrics such as the exact match ratio,hamming loss,zero-one-loss,precision,recall,F1-score,and weighted *** results reveal ERNIE is the top-performing model,achieving an exact match ratio of 72.32%,an accuracy rate of 87.17%,and 84.41%of *** findings demonstrate that the tested models substantially outperform other state-of-the-art studies,enhancing the accuracy by at least 3%and confirming them as powerful tools for personality *** findings represent substantial a
维普期刊数据库