Web Mining For Financial Market Prediction Based On Online Sentiments
作者： Tuo Li , Bing Jiang , Cheng Cheng , Wei Xu
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摘要: Financial market prediction is a critically important research topic in financial data mining because of its potential commerce application and attractive profits. Previous studies mainly focus on economic indicators. Web information, as an information repository, has been used customer relationship management recommendation, but it rarely considered to be useful prediction. In this paper, combined web sentiment analysis method proposed forecast markets using information. the method, spider firstly employed crawl tweets from Twitter. Secondly, Opinion Finder offered online sentiments hidden tweets. Thirdly, some new indicators are suggested stochastic time effective function (STEF) introduced integrate everyday sentiments. Fourthly, support vector regressions (SVRs) model between prices. Finally, selective can serviced for To validate Standard Poor’s 500 Index (S&P 500) evaluation. The empirical results show that our forecasting outperforms traditional methods, meanwhile, also capture individual behavior quickly easily. These findings imply promising approach
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