{"title":"通过增强话题和角色来模拟社交网络中的群体级公众情绪","authors":"","doi":"10.1016/j.knosys.2024.112594","DOIUrl":null,"url":null,"abstract":"<div><div>Public sentiment within social networks exerts a profound influence on societal dynamics, underscoring the increasing demand for accurate public opinion prediction. Most existing methods predominantly measure sentiment by quantifying user sentiments individually, overlooking group-level factors that crucially contribute to public sentiment. Thus, based on our finding that public sentiment is primarily shaped by user-group interactions and their interplay with evolving topics, we innovatively model the forming process of public sentiment at the group level. In this paper, we propose the Topic and Role Enhanced Group-level Public Sentiment Prediction model (TRESP), capturing the intricate interplay among sentiment, topic, and role. Specifically, an LSTM neural network is firstly leveraged to trace the temporal correlations between topics and sentiment shifts, yielding a topic-informed content sentiment representation. Subsequently, a specially crafted hierarchical attention network captures social and role attributes, representing the overarching social group environment. Finally, we predict future public sentiment by merging the derived group sentiment representation with the group social representation, demonstrating a holistic insight into the sentiment trajectory. Extensive experiments were conducted on two real-world datasets of over 30,000 tweets collected from more than 14,000 users to validate our model. Notably, our model significantly outperforms the state-of-the-art approaches in public sentiment prediction, indicating the importance and effectiveness of encapsulating interactions both within and among user subgroups.</div></div>","PeriodicalId":49939,"journal":{"name":"Knowledge-Based Systems","volume":null,"pages":null},"PeriodicalIF":7.2000,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Modeling group-level public sentiment in social networks through topic and role enhancement\",\"authors\":\"\",\"doi\":\"10.1016/j.knosys.2024.112594\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Public sentiment within social networks exerts a profound influence on societal dynamics, underscoring the increasing demand for accurate public opinion prediction. Most existing methods predominantly measure sentiment by quantifying user sentiments individually, overlooking group-level factors that crucially contribute to public sentiment. Thus, based on our finding that public sentiment is primarily shaped by user-group interactions and their interplay with evolving topics, we innovatively model the forming process of public sentiment at the group level. In this paper, we propose the Topic and Role Enhanced Group-level Public Sentiment Prediction model (TRESP), capturing the intricate interplay among sentiment, topic, and role. Specifically, an LSTM neural network is firstly leveraged to trace the temporal correlations between topics and sentiment shifts, yielding a topic-informed content sentiment representation. Subsequently, a specially crafted hierarchical attention network captures social and role attributes, representing the overarching social group environment. Finally, we predict future public sentiment by merging the derived group sentiment representation with the group social representation, demonstrating a holistic insight into the sentiment trajectory. Extensive experiments were conducted on two real-world datasets of over 30,000 tweets collected from more than 14,000 users to validate our model. Notably, our model significantly outperforms the state-of-the-art approaches in public sentiment prediction, indicating the importance and effectiveness of encapsulating interactions both within and among user subgroups.</div></div>\",\"PeriodicalId\":49939,\"journal\":{\"name\":\"Knowledge-Based Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.2000,\"publicationDate\":\"2024-10-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Knowledge-Based Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0950705124012280\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Knowledge-Based Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0950705124012280","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Modeling group-level public sentiment in social networks through topic and role enhancement
Public sentiment within social networks exerts a profound influence on societal dynamics, underscoring the increasing demand for accurate public opinion prediction. Most existing methods predominantly measure sentiment by quantifying user sentiments individually, overlooking group-level factors that crucially contribute to public sentiment. Thus, based on our finding that public sentiment is primarily shaped by user-group interactions and their interplay with evolving topics, we innovatively model the forming process of public sentiment at the group level. In this paper, we propose the Topic and Role Enhanced Group-level Public Sentiment Prediction model (TRESP), capturing the intricate interplay among sentiment, topic, and role. Specifically, an LSTM neural network is firstly leveraged to trace the temporal correlations between topics and sentiment shifts, yielding a topic-informed content sentiment representation. Subsequently, a specially crafted hierarchical attention network captures social and role attributes, representing the overarching social group environment. Finally, we predict future public sentiment by merging the derived group sentiment representation with the group social representation, demonstrating a holistic insight into the sentiment trajectory. Extensive experiments were conducted on two real-world datasets of over 30,000 tweets collected from more than 14,000 users to validate our model. Notably, our model significantly outperforms the state-of-the-art approaches in public sentiment prediction, indicating the importance and effectiveness of encapsulating interactions both within and among user subgroups.
期刊介绍:
Knowledge-Based Systems, an international and interdisciplinary journal in artificial intelligence, publishes original, innovative, and creative research results in the field. It focuses on knowledge-based and other artificial intelligence techniques-based systems. The journal aims to support human prediction and decision-making through data science and computation techniques, provide a balanced coverage of theory and practical study, and encourage the development and implementation of knowledge-based intelligence models, methods, systems, and software tools. Applications in business, government, education, engineering, and healthcare are emphasized.