{"title":"从社交媒体到股票市场的情绪相关性发现","authors":"S. Xie, Manshu Li, Jianxin Li","doi":"10.1145/3290688.3290712","DOIUrl":null,"url":null,"abstract":"Social media data analytics have been successfully applied in many real applications such as product recommendation, target advertisement. In recent years, it also attracted lots of attention from the financial researchers to analyse the financial trending or stock marketing prediction. In this paper, our goal is to investigate the meaningful way of uncovering the correlation between the stock share price change and the social media data usage. In this work, we first provide a mechanism to collect Twitter data, use Latent Dirichlet Allocation for topic modelling, then perform the sentiment analysis based on topics, and finally discover the correlation between social media and share price. Based on our empirical results, we find that the correlation could be impacted by the popularity of discussion as well as the valence of community, which represents the happiness to the target companies to be analysed and predicted. This could be built up by exploring the market and crisis resolution. The influence of online social users also plays a significant role in the correlation, which is a factor of manipulation that the influential users should be considered by measuring the responsibility of their social media account.","PeriodicalId":297760,"journal":{"name":"Proceedings of the Australasian Computer Science Week Multiconference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Sentiment Correlation Discovery From Social Media to Share Market\",\"authors\":\"S. Xie, Manshu Li, Jianxin Li\",\"doi\":\"10.1145/3290688.3290712\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Social media data analytics have been successfully applied in many real applications such as product recommendation, target advertisement. In recent years, it also attracted lots of attention from the financial researchers to analyse the financial trending or stock marketing prediction. In this paper, our goal is to investigate the meaningful way of uncovering the correlation between the stock share price change and the social media data usage. In this work, we first provide a mechanism to collect Twitter data, use Latent Dirichlet Allocation for topic modelling, then perform the sentiment analysis based on topics, and finally discover the correlation between social media and share price. Based on our empirical results, we find that the correlation could be impacted by the popularity of discussion as well as the valence of community, which represents the happiness to the target companies to be analysed and predicted. This could be built up by exploring the market and crisis resolution. The influence of online social users also plays a significant role in the correlation, which is a factor of manipulation that the influential users should be considered by measuring the responsibility of their social media account.\",\"PeriodicalId\":297760,\"journal\":{\"name\":\"Proceedings of the Australasian Computer Science Week Multiconference\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-01-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Australasian Computer Science Week Multiconference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3290688.3290712\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Australasian Computer Science Week Multiconference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3290688.3290712","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Sentiment Correlation Discovery From Social Media to Share Market
Social media data analytics have been successfully applied in many real applications such as product recommendation, target advertisement. In recent years, it also attracted lots of attention from the financial researchers to analyse the financial trending or stock marketing prediction. In this paper, our goal is to investigate the meaningful way of uncovering the correlation between the stock share price change and the social media data usage. In this work, we first provide a mechanism to collect Twitter data, use Latent Dirichlet Allocation for topic modelling, then perform the sentiment analysis based on topics, and finally discover the correlation between social media and share price. Based on our empirical results, we find that the correlation could be impacted by the popularity of discussion as well as the valence of community, which represents the happiness to the target companies to be analysed and predicted. This could be built up by exploring the market and crisis resolution. The influence of online social users also plays a significant role in the correlation, which is a factor of manipulation that the influential users should be considered by measuring the responsibility of their social media account.