{"title":"面向市场新闻事件识别和趋势分析的文档聚类","authors":"Lipika Dey, Anuj Mahajan, S. M. Haque","doi":"10.1109/ICAPR.2009.84","DOIUrl":null,"url":null,"abstract":"In this paper we have proposed a stock market analysis system that analyzes financial news items to identify and characterize major events that impact the market. The events have been identified using Latent Dirichlet Allocation(LDA) based topic extraction mechanism. The topic-document data is then clustered using kernel k means algorithm. The clusters are analyzed jointly with the SENSEX raw data to extract major events and their effects. The system has been implemented on capital market news about the Indian share market of the past three years.","PeriodicalId":443926,"journal":{"name":"2009 Seventh International Conference on Advances in Pattern Recognition","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"Document Clustering for Event Identification and Trend Analysis in Market News\",\"authors\":\"Lipika Dey, Anuj Mahajan, S. M. Haque\",\"doi\":\"10.1109/ICAPR.2009.84\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we have proposed a stock market analysis system that analyzes financial news items to identify and characterize major events that impact the market. The events have been identified using Latent Dirichlet Allocation(LDA) based topic extraction mechanism. The topic-document data is then clustered using kernel k means algorithm. The clusters are analyzed jointly with the SENSEX raw data to extract major events and their effects. The system has been implemented on capital market news about the Indian share market of the past three years.\",\"PeriodicalId\":443926,\"journal\":{\"name\":\"2009 Seventh International Conference on Advances in Pattern Recognition\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-02-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 Seventh International Conference on Advances in Pattern Recognition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICAPR.2009.84\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 Seventh International Conference on Advances in Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAPR.2009.84","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Document Clustering for Event Identification and Trend Analysis in Market News
In this paper we have proposed a stock market analysis system that analyzes financial news items to identify and characterize major events that impact the market. The events have been identified using Latent Dirichlet Allocation(LDA) based topic extraction mechanism. The topic-document data is then clustered using kernel k means algorithm. The clusters are analyzed jointly with the SENSEX raw data to extract major events and their effects. The system has been implemented on capital market news about the Indian share market of the past three years.
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