Method for measuring the correlation degree between stock information news headwords and related stocks
A measurement method and stock technology, applied in digital data information retrieval, special data processing applications, instruments, etc., can solve the problem of high cost of dictionaries, high frequency of co-occurrence of word pairs, and inability to guarantee the correlation between stock information news center words Measure the quality of stock correlation results and other issues to achieve the effect of high computational efficiency
Inactive Publication Date: 2019-03-22
FUJIAN UNIV OF TECH
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Problems solved by technology
Since there is no semantic dictionary in the field of stocks at present, and the construction of a dictionary in the field of stocks is costly and time-consuming, the calculation method of word association degree based on semantic dictionary is not suitable for measuring the association degree of stock information news center words and related stocks
In addition, the traditional statistics-based word association calculation method cannot take into account the high and low co-occurrence frequency of word pairs at the same time, and cannot guarantee the measurement quality of stock information news center words and related stock association results
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[0049] refer to Figure 1 to Figure 4 , a method for measuring the correlation between stock information news keywords and related stocks, including:
[0050] Step S10, read the data in the prepared stock information news file, and construct the transaction database D, D={T 1 , T 2 , T 3 ,...,T i}, transaction T i Represents an item set composed of keywords from the same stock information news, i∈[1,n], n represents the number of relevant stock information news included in the stock information news file; keywords include central words and tag words ; Among them, the data in the stock information news file is composed of multiple lines of central words and tagged words, and the central words and tagged words in each row are from the same news, and the words are separated by spaces.
[0051] Step S20, exhaust all frequent itemsets from transaction database D, and generate frequent itemsets database L and frequent itemsets group L k , L={L 1 , L 2 , L 3 ,...,L k}, L k...
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The invention provides a method for measuring the correlation degree between stock information news headwords and related stocks. The method comprises the following steps: step S10, reading data in aprepared stock information news file and constructing a transaction database D; Step S20, exhausting all frequent itemsets from the transaction database D, and generating a frequent itemset database Land a frequent itemset group Lk; Step S30, calculating a plurality of association rules alpha, beta with co-occurrence relationship from the frequent itemsets Fk, m, wherein the itemset alpha is a non-empty proper subset of Fk. M, the itemset beta is a complement set of the itemset alpha with respect to the frequent itemset Fk. M, and classifying the association rules alpha ,beta into a word co-occurrence database. The method of the invention counts the times of the stock information news keywords and the related stocks in different news through a mining algorithm, and uses the correlation degree formula to carry out the correlation degree quantity of the stock information news keywords and the related stocks, so that the calculation efficiency is high, and the calculation is fast and reliable. The invention also discloses a correlation degree quantity method of the stock information news keywords and the related stocks.
Description
technical field [0001] The invention relates to the technical field of stock data mining, in particular to a method for measuring the association between stock information news central words and related stocks. Background technique [0002] A collection of items is called an item set; an item set containing k items is called a k-itemset; an item set whose support is greater than the minimum support threshold is a frequent item set; the item frequency of an item set is the number of transactions containing the item set , referred to as frequency, support count or count of itemsets for short. An association rule is an implication of the form X→Y, where X and Y are called the predecessor and successor of the association rule, respectively. [0003] With the rapid development of information technology and the popularization of the Internet, various news information about stocks has expanded rapidly. How to quickly and accurately obtain the needed useful information from massiv...
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Inventor 王家华薛醒思詹先银朱钟元范淑娟刘艳萍杨莹
Owner FUJIAN UNIV OF TECH