Calculation method and system for relevance degree of individual share and article

Abstract

The present invention discloses a calculation method and system for relevance degree of an individual share and an article. The system comprises a data acquisition module, a relevance degree analysis module, an emotion analysis module, a heat analysis module, a data storage module and a data retrieval module. The method comprises: acquiring massive financial news corpus every day; performing text data mining; analyzing a relevance between an individual share and an article among the corpus that is acquired in real time; analyzing individual share emotion in the corpus that is acquired in real time; summarizing the relevance between the individual share and the article, i.e. heat of the individual share daily; enabling an investor to retrieve market condition news of a share that draws the attention of the investor, and providing the relevance degree with the article, and historical heat indicators of the emotion and share for reference, so that the method and system become an excellent retrieval tool of the investor for individual share market condition news.

Description

technical field [0001] The invention relates to the correlation analysis between entities and massive articles, in particular to a correlation analysis method and system for individual stock entities and articles. Background technique [0002] At present, information related to individual stocks is mainly retrieved through search engine tools. This method has the following disadvantages: [0003] 1. Loud noise: There are many promotional links in advertisements. Generally, the search results of advertisements are ranked at the top; semantic errors, search engines mainly obtain search results based on keywords, not at the semantic level. When there is ambiguity, there are many inaccurate results. [0004] 2. High degree: For hot news information, all major websites will report; in many cases, the title and content are exactly the same, but search engines will not help you remove duplication. [0005] 3. Lack of key indicators: The results of search engines generally only sh...

AI Technical Summary

Problems solved by technology

[0003] 1. Loud noise: There are many promotional links in advertisements. Generally, the search results of advertisements are ranked at the top; semantic errors, search engines mainly obtain search results based on keywords, not at the semantic level. When there is ambiguity, there are many inaccurate results

[0004] 2. High degree: For hot news information, all major websites will report; in many cases, the title and content are exactly the same, but search engines will not help you remove duplication

[0005] 3. Lack of key indicators: The results of search engines generally only show that the article contains the keyword you searched for (here is a stock), but does not indicate how much the stock is related to the article, emotional information (good or bad), investment Investors also need to spend a lot of time and energy to read and analyze before making investment decisions

[0006] 4. No time track: Search engines do not have a time dimension. For stocks that investors care about, they can only obtain current articles and cannot check historical popularity, which is not conducive to investors' decision-making choices

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