With the highly integration of the Internet world and the real world, Internet information not only provides real-time and effective data for financial investors, but also helps them understand market dynamics, and en...With the highly integration of the Internet world and the real world, Internet information not only provides real-time and effective data for financial investors, but also helps them understand market dynamics, and enables investors to quickly identify relevant financial events that may lead to stock market volatility. However, in the research of event detection in the financial field, many studies are focused on micro-blog, news and other network text information. Few scholars have studied the characteristics of financial time series data. Considering that in the financial field, the occurrence of an event often affects both the online public opinion space and the real transaction space, so this paper proposes a multi-source heterogeneous information detection method based on stock transaction time series data and online public opinion text data to detect hot events in the stock market. This method uses outlier detection algorithm to extract the time of hot events in stock market based on multi-member fusion. And according to the weight calculation formula of the feature item proposed in this paper, this method calculates the keyword weight of network public opinion information to obtain the core content of hot events in the stock market. Finally, accurate detection of stock market hot events is achieved.展开更多
At present, there are some resistible illegal operations aiming at creating false public opinions in internet public opinions on emergent event, which seriously disrupted the normal Internet order. However, the tradit...At present, there are some resistible illegal operations aiming at creating false public opinions in internet public opinions on emergent event, which seriously disrupted the normal Internet order. However, the traditional research method of internet public opinion pre-waming mainly relies on manual analysis, which is too inefficient to adapt to the analysis of massive internet public opinion information. According to the above analysis, this paper puts forward an internet public opinion pre-warning mechanism on emergent event based on multi-relational data clustering algorithm, discusses the specific pre-waming from the aspects of the state and dissemination of internet public opinions and the historical data, and automatically classifies the internet public opinions through multi-relational data clustering algorithm. And the results show that such method can be used to effectively study the internet public opinion pre-waming on emergent event, with the accuracy rate of as high as 95%.展开更多
After the occurrence of unexpected group events of network, the relevant opinion information will spread rapidly through micro-blog, and the negative public opinion information will aggravate the unexpected the group ...After the occurrence of unexpected group events of network, the relevant opinion information will spread rapidly through micro-blog, and the negative public opinion information will aggravate the unexpected the group events to upgrade and expand the scope of harm. It is difficult to deal. So public opinion control is very important. In this paper, we establish an influence model for spreading of public opinion based on SIR model. Through the political analysis, this paper finds that the network group events will subside, but the influence scope, time and ability of event cannot be ignored. As a result of this study, the corresponding strategies are put forward in this paper.展开更多
文摘With the highly integration of the Internet world and the real world, Internet information not only provides real-time and effective data for financial investors, but also helps them understand market dynamics, and enables investors to quickly identify relevant financial events that may lead to stock market volatility. However, in the research of event detection in the financial field, many studies are focused on micro-blog, news and other network text information. Few scholars have studied the characteristics of financial time series data. Considering that in the financial field, the occurrence of an event often affects both the online public opinion space and the real transaction space, so this paper proposes a multi-source heterogeneous information detection method based on stock transaction time series data and online public opinion text data to detect hot events in the stock market. This method uses outlier detection algorithm to extract the time of hot events in stock market based on multi-member fusion. And according to the weight calculation formula of the feature item proposed in this paper, this method calculates the keyword weight of network public opinion information to obtain the core content of hot events in the stock market. Finally, accurate detection of stock market hot events is achieved.
文摘At present, there are some resistible illegal operations aiming at creating false public opinions in internet public opinions on emergent event, which seriously disrupted the normal Internet order. However, the traditional research method of internet public opinion pre-waming mainly relies on manual analysis, which is too inefficient to adapt to the analysis of massive internet public opinion information. According to the above analysis, this paper puts forward an internet public opinion pre-warning mechanism on emergent event based on multi-relational data clustering algorithm, discusses the specific pre-waming from the aspects of the state and dissemination of internet public opinions and the historical data, and automatically classifies the internet public opinions through multi-relational data clustering algorithm. And the results show that such method can be used to effectively study the internet public opinion pre-waming on emergent event, with the accuracy rate of as high as 95%.
文摘After the occurrence of unexpected group events of network, the relevant opinion information will spread rapidly through micro-blog, and the negative public opinion information will aggravate the unexpected the group events to upgrade and expand the scope of harm. It is difficult to deal. So public opinion control is very important. In this paper, we establish an influence model for spreading of public opinion based on SIR model. Through the political analysis, this paper finds that the network group events will subside, but the influence scope, time and ability of event cannot be ignored. As a result of this study, the corresponding strategies are put forward in this paper.
