{"title":"利用基于中观情感分析、PSO 和层次 LSTM 的情境感知型内乱事件预测","authors":"Pratima Singh;Amita Jain","doi":"10.1109/TCSS.2023.3338509","DOIUrl":null,"url":null,"abstract":"Civil unrest is among the important hurdles in the countries’ progress as it deteriorates gross domestic product (GDP), international relations, foreign direct investment (FDI), globalization, public opinion, tourism, and businesses. Due to civil unrest a variety of serious problems, viz. loss of life/injury, resources, political stability, and human rights occur. Recently, few researchers have given insights on the prediction of occurrences of civil unrest events by using hypothesis testing and some basic machine/deep learning models. Important factors such as people’s emotions/sentiments, contextual information, and civil unrest events feature’ importance are ignored presently. For the first time, the proposed work overcomes all these research gaps by hybridizing the neutrosophic set, aspect-based sentiment analysis, particle swarm optimization (PSO), and hierarchical long short-term memory (hierarchical LSTM). Neutrosophic set along with aspect-based sentiment analysis has been used to get the sentiment and features’ importance. The resulting features’ weights have been optimized using PSO. For a more comprehensive understanding of the input sequence and feature weights, hierarchical LSTM has been used. Doing so obtained results that are more accurately improved for civil unrest events prediction. The performance of the proposed model has been evaluated and compared with state of art methods. Experimentation and evaluation show the proposed model outperforms the baseline methods by 3% to 15% on the standard datasets in terms of accuracy.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":4.5000,"publicationDate":"2023-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Context-Aware Civil Unrest Event Prediction Using Neutrosophic-Aspect-Based Sentiment Analysis, PSO, and Hierarchical LSTM\",\"authors\":\"Pratima Singh;Amita Jain\",\"doi\":\"10.1109/TCSS.2023.3338509\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Civil unrest is among the important hurdles in the countries’ progress as it deteriorates gross domestic product (GDP), international relations, foreign direct investment (FDI), globalization, public opinion, tourism, and businesses. Due to civil unrest a variety of serious problems, viz. loss of life/injury, resources, political stability, and human rights occur. Recently, few researchers have given insights on the prediction of occurrences of civil unrest events by using hypothesis testing and some basic machine/deep learning models. Important factors such as people’s emotions/sentiments, contextual information, and civil unrest events feature’ importance are ignored presently. For the first time, the proposed work overcomes all these research gaps by hybridizing the neutrosophic set, aspect-based sentiment analysis, particle swarm optimization (PSO), and hierarchical long short-term memory (hierarchical LSTM). Neutrosophic set along with aspect-based sentiment analysis has been used to get the sentiment and features’ importance. The resulting features’ weights have been optimized using PSO. For a more comprehensive understanding of the input sequence and feature weights, hierarchical LSTM has been used. Doing so obtained results that are more accurately improved for civil unrest events prediction. The performance of the proposed model has been evaluated and compared with state of art methods. Experimentation and evaluation show the proposed model outperforms the baseline methods by 3% to 15% on the standard datasets in terms of accuracy.\",\"PeriodicalId\":13044,\"journal\":{\"name\":\"IEEE Transactions on Computational Social Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.5000,\"publicationDate\":\"2023-12-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Computational Social Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10375812/\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, CYBERNETICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Computational Social Systems","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10375812/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, CYBERNETICS","Score":null,"Total":0}
Context-Aware Civil Unrest Event Prediction Using Neutrosophic-Aspect-Based Sentiment Analysis, PSO, and Hierarchical LSTM
Civil unrest is among the important hurdles in the countries’ progress as it deteriorates gross domestic product (GDP), international relations, foreign direct investment (FDI), globalization, public opinion, tourism, and businesses. Due to civil unrest a variety of serious problems, viz. loss of life/injury, resources, political stability, and human rights occur. Recently, few researchers have given insights on the prediction of occurrences of civil unrest events by using hypothesis testing and some basic machine/deep learning models. Important factors such as people’s emotions/sentiments, contextual information, and civil unrest events feature’ importance are ignored presently. For the first time, the proposed work overcomes all these research gaps by hybridizing the neutrosophic set, aspect-based sentiment analysis, particle swarm optimization (PSO), and hierarchical long short-term memory (hierarchical LSTM). Neutrosophic set along with aspect-based sentiment analysis has been used to get the sentiment and features’ importance. The resulting features’ weights have been optimized using PSO. For a more comprehensive understanding of the input sequence and feature weights, hierarchical LSTM has been used. Doing so obtained results that are more accurately improved for civil unrest events prediction. The performance of the proposed model has been evaluated and compared with state of art methods. Experimentation and evaluation show the proposed model outperforms the baseline methods by 3% to 15% on the standard datasets in terms of accuracy.
期刊介绍:
IEEE Transactions on Computational Social Systems focuses on such topics as modeling, simulation, analysis and understanding of social systems from the quantitative and/or computational perspective. "Systems" include man-man, man-machine and machine-machine organizations and adversarial situations as well as social media structures and their dynamics. More specifically, the proposed transactions publishes articles on modeling the dynamics of social systems, methodologies for incorporating and representing socio-cultural and behavioral aspects in computational modeling, analysis of social system behavior and structure, and paradigms for social systems modeling and simulation. The journal also features articles on social network dynamics, social intelligence and cognition, social systems design and architectures, socio-cultural modeling and representation, and computational behavior modeling, and their applications.