Heuristic personality recognition based on fusing multiple conversations and utterance-level affection

IF 7.4 1区 管理学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Haijun He, Bobo Li, Yiyun Xiong, Li Zheng, Kang He, Fei Li, Donghong Ji
{"title":"Heuristic personality recognition based on fusing multiple conversations and utterance-level affection","authors":"Haijun He,&nbsp;Bobo Li,&nbsp;Yiyun Xiong,&nbsp;Li Zheng,&nbsp;Kang He,&nbsp;Fei Li,&nbsp;Donghong Ji","doi":"10.1016/j.ipm.2024.103931","DOIUrl":null,"url":null,"abstract":"<div><div><strong>P</strong>ersonality <strong>R</strong>ecognition in <strong>C</strong>onversations (<strong>PRC</strong>) is a task of significant interest and practical value. Existing studies on the PRC task utilize conversation inadequately and neglect affective information. Considering the way of information processing of these studies is not yet close enough to the concept of personality, we propose the SAH-GCN model for the PRC task in this study. This model initially processes the original conversation input to extract the central speaker feature. Leveraging Contrastive Learning, it continuously adjusts the embedding of each utterance by incorporating affective information to cope with the semantic similarity. Subsequently, the model employs Graph Convolutional Networks to simulate the conversation dynamics, ensuring comprehensive interaction between the central speaker feature and other relevant features. Lastly, it heuristically fuses central speaker features from multiple conversations involving the same speaker into one comprehensive feature, facilitating personality recognition. We conduct experiments using the recently released CPED dataset, which is the personality dataset encompassing affection labels and conversation details. Our results demonstrate that SAH-GCN achieves superior accuracy (+1.88%) compared to prior works on the PRC task. Further analysis verifies the efficacy of our scheme that fuses multiple conversations and incorporates affective information for personality recognition.</div></div>","PeriodicalId":50365,"journal":{"name":"Information Processing & Management","volume":null,"pages":null},"PeriodicalIF":7.4000,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Processing & Management","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0306457324002905","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

Abstract

Personality Recognition in Conversations (PRC) is a task of significant interest and practical value. Existing studies on the PRC task utilize conversation inadequately and neglect affective information. Considering the way of information processing of these studies is not yet close enough to the concept of personality, we propose the SAH-GCN model for the PRC task in this study. This model initially processes the original conversation input to extract the central speaker feature. Leveraging Contrastive Learning, it continuously adjusts the embedding of each utterance by incorporating affective information to cope with the semantic similarity. Subsequently, the model employs Graph Convolutional Networks to simulate the conversation dynamics, ensuring comprehensive interaction between the central speaker feature and other relevant features. Lastly, it heuristically fuses central speaker features from multiple conversations involving the same speaker into one comprehensive feature, facilitating personality recognition. We conduct experiments using the recently released CPED dataset, which is the personality dataset encompassing affection labels and conversation details. Our results demonstrate that SAH-GCN achieves superior accuracy (+1.88%) compared to prior works on the PRC task. Further analysis verifies the efficacy of our scheme that fuses multiple conversations and incorporates affective information for personality recognition.
基于融合多对话和语篇级情感的启发式人格识别
对话中的人格识别(PRC)是一项具有重大意义和实用价值的任务。现有的关于人格识别任务的研究对对话的利用不够充分,忽略了情感信息。考虑到这些研究的信息处理方式还不够贴近人格的概念,我们在本研究中提出了针对 PRC 任务的 SAH-GCN 模型。该模型对原始对话输入进行初步处理,以提取说话者的中心特征。利用对比学习(Contrastive Learning)技术,该模型会结合情感信息不断调整每句话的嵌入,以应对语义相似性问题。随后,该模型采用图卷积网络来模拟对话动态,确保中心发言人特征与其他相关特征之间的全面互动。最后,它启发式地将涉及同一发言人的多个对话中的中心发言人特征融合为一个综合特征,从而促进个性识别。我们使用最近发布的 CPED 数据集进行了实验,该数据集是包含感情标签和对话细节的个性数据集。结果表明,在 PRC 任务中,SAH-GCN 的准确率(+1.88%)优于之前的研究。进一步的分析验证了我们融合多个对话和情感信息的人格识别方案的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Information Processing & Management
Information Processing & Management 工程技术-计算机:信息系统
CiteScore
17.00
自引率
11.60%
发文量
276
审稿时长
39 days
期刊介绍: Information Processing and Management is dedicated to publishing cutting-edge original research at the convergence of computing and information science. Our scope encompasses theory, methods, and applications across various domains, including advertising, business, health, information science, information technology marketing, and social computing. We aim to cater to the interests of both primary researchers and practitioners by offering an effective platform for the timely dissemination of advanced and topical issues in this interdisciplinary field. The journal places particular emphasis on original research articles, research survey articles, research method articles, and articles addressing critical applications of research. Join us in advancing knowledge and innovation at the intersection of computing and information science.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信