WO2019218517A1 - Server, method for processing text data and storage medium - Google Patents

Abstract

The present application relates to a server, a method for processing text data, and a storage medium, the method comprising: sorting various financial text data into corresponding text object types; analyzing financial text data of each text object type of each stock entity at each time point to obtain an evaluation grade of each piece of financial text data; counting the number of each evaluation grade of the financial text data under each text object type, and calculating the proportion of each evaluation grade on the basis of the counted number of each evaluation grade; obtaining attribute scores of each evaluation grade, and calculating a market evaluation index of the stock entities at the time points according to the attribute score corresponding to each evaluation grade and the proportion of each evaluation grade; obtaining a market evaluation index of the stock entities at each time point, and sorting the market evaluation index at each time point in chronological order to generate a market evaluation index sequence corresponding to the stock entities. The present application may fully mine financial text data to obtain accurate market information.

Description

Server, text data processing method and storage medium

Priority claim

The present application is based on the priority of the Chinese Patent Application entitled "Server, Text Data Processing Method and Storage Medium", which is filed on May 16, 2018, with the application number of CN201810469419X, the entire contents of which are hereby incorporated by reference. The way is combined in this application.

Technical field

The present application relates to the field of data analysis technologies, and in particular, to a server, a method for processing text data, and a storage medium.

Background technique

At present, in each time section, each listed company has various text data, such as performance forecast, financing report, analyst forecast, corporate governance, etc. In the prior art, generally only a single text is analyzed to obtain the corresponding market. Evaluation, however, because these text data contain a large amount of market information, simple analysis of individual texts can not fully exploit accurate market information, and can not effectively guide the company or industry, so the text data is fully tapped to get accurate Market information has become a technical issue to be resolved.

Summary of the invention

The purpose of the present application is to provide a server, a method for processing text data, and a storage medium, which are intended to fully exploit financial text data to obtain accurate market information.

To achieve the above object, the present application provides a server including a memory and a processor coupled to the memory, the memory storing a processing system operable on the processor, the processing system being The processor implements the following steps when executed:

The various financial text data are classified into corresponding text object types according to a preset classification rule, wherein the text object types include a performance type, a financing type, a corporate governance type, an analyst type, and other types;

Using a predetermined text analysis method, analyzing financial text data of each text object type of each stock entity at each time point, and obtaining an evaluation level corresponding to each financial text data;

The number of each evaluation level of the financial text data under each text object type is counted, and the proportion of each evaluation level is calculated based on the number of each evaluation level after the statistics;

Obtaining an attribute score corresponding to each evaluation level, and calculating a market evaluation index of the stock entity at the time point according to the attribute score corresponding to each evaluation level and the proportion of each evaluation level;

Obtain the market evaluation index of the stock entity at each time point, and generate the market evaluation index sequence corresponding to the stock entity in the chronological order of the market evaluation index at each time point.

To achieve the above object, the present application further provides a method for processing text data, and the method for processing the text data includes:

S1, classifying various financial text data into corresponding text object types according to a preset classification rule, wherein the text object types include a performance type, a financing type, a corporate governance type, an analyst type, and other types;

S2, analyzing financial text data of each text object type of each stock entity at each time point by using a predetermined text analysis method, and obtaining an evaluation level corresponding to each financial text data;

S3, the number of each evaluation level of the financial text data under each text object type is counted, and the proportion of each evaluation level is calculated based on the number of each evaluation level after the statistics;

S4, obtaining an attribute score corresponding to each evaluation level, and calculating a market evaluation index of the stock entity at the time point according to the attribute score corresponding to each evaluation level and the proportion of each evaluation level;

S5: Obtain a market evaluation index of the stock entity at each time point, and generate a market evaluation index sequence corresponding to the stock entity in a chronological order by the market evaluation index at each time point.

The present application also provides a computer readable storage medium having stored thereon a processing system, the processing system being implemented by a processor to implement the steps of the text data processing method described above.

The beneficial effects of the present application are as follows: the method for dividing financial text data into different text object types and analyzing by using a predetermined text analysis method can fully exploit accurate market information and generate market evaluation index sequences in chronological order. The changes and trends of the market's evaluation of the company can be derived for market analysis.

DRAWINGS

1 is a schematic diagram of a hardware architecture of an embodiment of a server according to the present application;

2 is a schematic flowchart of a first embodiment of a method for processing text data according to the present application;

FIG. 3 is a schematic diagram showing the refinement process of step S2 shown in FIG. 2;

FIG. 4 is a schematic flowchart diagram of a second embodiment of a method for processing text data according to the present application.

Detailed ways

In order to make the objects, technical solutions, and advantages of the present application more comprehensible, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It is understood that the specific embodiments described herein are merely illustrative of the application and are not intended to be limiting. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present application without departing from the inventive scope are the scope of the present application.

It should be noted that the descriptions of "first", "second" and the like in the present application are for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. . Thus, features defining "first" or "second" may include at least one of the features, either explicitly or implicitly. In addition, the technical solutions between the various embodiments may be combined with each other, but must be based on the realization of those skilled in the art, and when the combination of the technical solutions is contradictory or impossible to implement, it should be considered that the combination of the technical solutions does not exist. Nor is it within the scope of protection required by this application.

Referring to FIG. 1, which is a schematic diagram of a hardware architecture of an embodiment of the server of the present application, the server 1 is a device capable of automatically performing numerical calculation and/or information processing according to an instruction set or stored in advance. The server 1 may be a computer, a single network server, a server group composed of multiple network servers, or a cloud-based cloud composed of a large number of hosts or network servers, where cloud computing is a type of distributed computing. A super virtual computer consisting of a group of loosely coupled computers.

In the present embodiment, the server 1 may include, but is not limited to, a memory 11, a processor 12, and a network interface 13 communicably connected to each other through a system bus, and the memory 11 stores a processing system operable on the processor 12. It is to be noted that Figure 1 shows only server 1 with components 11-13, but it should be understood that not all illustrated components may be implemented and that more or fewer components may be implemented instead.

The memory 11 includes a memory and at least one type of readable storage medium. The memory provides a cache for the operation of the server 1; the readable storage medium can be, for example, a flash memory, a hard disk, a multimedia card, a card type memory (for example, SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM). A non-volatile storage medium such as a read only memory (ROM), an electrically erasable programmable read only memory (EEPROM), a programmable read only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, or the like. In some embodiments, the readable storage medium may be an internal storage unit of the server 1, such as a hard disk of the server 1; in other embodiments, the non-volatile storage medium may also be an external storage device of the server 1, For example, a plug-in hard disk provided on the server 1, a smart memory card (SMC), a Secure Digital (SD) card, a flash card, and the like. In this embodiment, the readable storage medium of the memory 11 is generally used to store an operating system installed on the server 1 and various types of application software, such as program code for storing the processing system in an embodiment of the present application. Further, the memory 11 can also be used to temporarily store various types of data that have been output or are to be output.

The processor 12 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 12 is typically used to control the overall operation of the server 1, such as performing control and processing related to data interaction or communication with the other devices. In this embodiment, the processor 12 is configured to run program code or process data stored in the memory 11, such as running a processing system or the like.

The network interface 13 may comprise a wireless network interface or a wired network interface, which is typically used to establish a communication connection between the server 1 and other electronic devices.

The processing system is stored in the memory 11 and includes at least one computer readable instruction stored in the memory 11, the at least one computer readable instruction being executable by the processor 12 to implement the methods of various embodiments of the present application; The at least one computer readable instruction can be classified into different logic modules depending on the functions implemented by its various parts.

In an embodiment, when the processing system is executed by the processor 12, the following steps are implemented:

The various financial text data are classified into corresponding text object types according to a preset classification rule, wherein the text object types include a performance type, a financing type, a corporate governance type, an analyst type, and other types;

The default classification rule is to classify the text object type of the performance-related financial text data as the performance type, the text object type of the financing-related financial text data into the financing type, and the text of the financial text data related to the corporate governance. The object type is classified as the corporate governance type, the text object type of the financial text data related to the analyst is classified as the analyst type, and the text object type of the financial text data other than the above four types is classified into other types, as shown in Table 1 below. Show:

Table 1

Among them, the performance types include performance report, performance report, and performance exceeding expectations. The types of financing include: private placement and targeted breaks. The types of corporate governance include: executives increase and decrease, shareholder reduction, equity incentives, employee holdings, analysis The types of divisions include a sharp increase in earnings forecasts and sudden concern by analysts. Other types include: high delivery, index component adjustments, early disclosure of annual reports, and long-term announcements.

Using a predetermined text analysis method, analyzing financial text data of each text object type of each stock entity at each time point, and obtaining an evaluation level corresponding to each financial text data;

Among them, the individual stock entities are listed companies, and each stock entity will generate some financial text data at each time point. The time points can be every minute, every hour, every day, and so on.

In an embodiment, the step of analyzing the financial text data of each text object type of each stock entity at each time point by using a predetermined text analysis method, and obtaining the evaluation level corresponding to each financial text data, specifically includes:

Each financial text data is segmented by a predetermined word segmentation model to obtain a word segment corresponding to each financial text data; the word segment corresponding to each financial text data is input to a predetermined conversion model, and each financial text data corresponding to the output is obtained. a word vector; input a word vector corresponding to each financial text data into a predetermined sentiment analysis model, obtain an sentiment analysis result of each sentence in the output financial text data; and statistically analyze the sentiment analysis result of each statement in the financial text data And obtaining an evaluation level corresponding to the financial text data according to the calculated sentiment analysis result.

Wherein, the text of the financial text data is segmented by an already trained word segmentation model, and the word segmentation model is a trained neural network segmentation model, preferably a long-term and short-term memory cycle neural network. The process of training the neural network segmentation model includes: 1. Extracting a large number of well-written words from the corpus, wherein the model training uses predetermined segmentation corpora, such as the classic snippet corpus of Microsoft Research in bakeoff2005. 2. Train the training part and use the test part as the final test. 3. By comparing the input and output results of the neural network segmentation model (using the sequence labeling method) to judge the error of the model, if the test effect reaches 0.95 or above, the neural network segmentation model is completed.

Among them, the predetermined conversion model is the word2vec model, and the word2vec model includes a three-layer neural network, which can represent a word as a word vector and digitize the text. The word segment corresponding to the financial text data is input to the word2vec model to obtain a word vector corresponding to the financial text data.

The predetermined sentiment analysis model is a Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts. The main structure of the model is to input a sentence vector corresponding to a sentence text, after two layers. After the Convolutional Neural Network (CNN), it is transformed into a sentence-level vector, and then the vector is input into a 3-layer neural network, and the correct sentiment analysis result of the sentence is obtained through training.

In an embodiment, the sentiment analysis result includes three types, for example: [-1, 0, 1], wherein -1 indicates that the emotion expressed by the sentence is negative and negative, and 0 indicates that the emotion expressed by the sentence is biased. Neutral, 1 means that the expression expressed in the sentence is positive.

In addition, the output dimension of the 3-layer neural network can be adjusted by itself, which can be the above three-dimensional [-1, 0, 1], or two-dimensional [-1, 1], and its value is from -1 to 1. The preference for 1 means that the expression of the sentence is positive, and the bias of -1 means that the emotion expressed by the sentence is negative, negative, and so on.

The evaluation level of the financial text data includes the first level, the second level, and the third level, and the evaluation level may also be a good rating, a middle rating, and a bad rating. The first level corresponds to the above-described sentiment analysis result 1, and the second level corresponds to the above-described sentiment analysis result 0, corresponding to the above-described sentiment analysis result-1. Finally, the output of all sentences is fused together to calculate the total number of sentiment analysis results. If the total number of sentiment analysis results is the largest, the financial text data is the first level, and if the total number of sentiment analysis results is 0. At most, the financial text data is in the second level, and if the total number of sentiment analysis results is -1, the financial text data is in the third level.

The number of each evaluation level of the financial text data under each text object type is counted, and the proportion of each evaluation level is calculated based on the number of each evaluation level after the statistics;

The statistics of the number of evaluation levels of the financial text data under each text object type include: counting the number of the first level, the second level, and the third level of the financial text data under each text object type, Take the data of company A as an example, as shown in Table 2 below:

Company A	First level	second level	Third level	total
Performance category	3	1	0	-
Financing	0	2	0	-
Corporate Governance	1	1	3	-
Analyst	4	1	1	-
other	2	0	0	-
total	10	5	4	19
Generating weight	0.526316	0.263158	0.210526	-

Table 2

In Table 2, the number of the first level, the second level, and the third level is 10, 5, and 4, respectively, and the total number of evaluations is 10 + 5 + 4 = 19, and the proportion of the first level = 10 / 19 * 100%=52.63%, the specific gravity of the second grade=5/19*100%=26.32%, and the specific gravity of the third grade=4/19*100%=21.05%.

Obtaining an attribute score corresponding to each evaluation level, and calculating a market evaluation index of the stock entity at the time point according to the attribute score corresponding to each evaluation level and the proportion of each evaluation level;

The first level has an attribute score of 1, the second level has an attribute score of 0, the third level has an attribute score of -1, and the market evaluation index = 100* [the first level of the proportion *1 + second The proportion of the level *0 + the proportion of the third level * (-1)]. Taking the above company A as an example, the market evaluation index=100*(52.63%*1+26.32%*0+21.05%*(-1))=31.58. According to the market evaluation index, the market's evaluation of the company at that point in time can be obtained for market analysis.

Obtain the market evaluation index of the stock entity at each time point, and generate the market evaluation index sequence corresponding to the stock entity in the chronological order of the market evaluation index at each time point.

In this embodiment, according to the chronological order, a corresponding market evaluation index sequence is generated according to the market evaluation index of the above-mentioned individual entity, and a market evaluation index sequence of the company to which the individual entity belongs is obtained, and according to the market evaluation index sequence, the market pair can be obtained. The company's evaluation of changes and trends for market analysis.

Compared with the prior art, the present application analyzes the financial text data of different text object types of each stock entity at each time section by using a predetermined text analysis method, and obtains an evaluation of each financial text data. The number of each evaluation level of the financial text data under the text object type is counted and the proportion of each evaluation level is calculated, and the market evaluation index of the individual entity at the time point is calculated according to the attribute score and the specific gravity of each evaluation level, according to the market The evaluation index can be used to evaluate the company's evaluation of the company at that point in time. This application can fully exploit the accurate market information by dividing the financial text data into different text object types and using predetermined text analysis methods. The sequence of market evaluation indexes is generated in chronological order, and the changes and trends of the evaluation of the company by the market can be obtained for market analysis.

In an embodiment, based on the foregoing embodiment, when the processing system is executed by the processor, the following steps are further implemented:

According to the predetermined industry classification method, each individual entity is divided into corresponding industry categories, the latest total market value of each individual entity is obtained, and the total market value corresponding to each industry category is calculated according to the latest total market value of each individual entity; according to the latest total of each individual entity Calculating the market value of the entity by calculating the market value and the total market value corresponding to the industry category to which the entity belongs; calculating the industry evaluation index of the entity at the time based on the market evaluation index and the market value of the entity at the time; Obtain the industry evaluation index of the stock entity at each time point, and generate the industry evaluation index sequence corresponding to the stock entity in the chronological order of the industry evaluation index at each time point.

Among them, the predetermined industry classification method is, for example, the Shenwan industry classification method. In an embodiment, all the entities in the Shanghai and Shenzhen stock exchanges can be divided into the following 28 industry categories, including: mining, chemical, steel, non-ferrous metals, building materials, architectural decoration, electrical equipment, mechanical equipment, national defense military, automobile , household appliances, textile and garment, light industry manufacturing, commercial trade, agriculture, forestry, animal husbandry and fishery, food and beverage, leisure services, medical and biological, public utilities, transportation, real estate, electronics, computers, media, communications, banking, non-banking finance, comprehensive .

Taking the industry category of the bank as an example, calculating the bank's industry evaluation index includes: first, extracting the latest total market value of each stock entity, adding the latest total market value of each stock entity to the total market value of the industry; second, calculating the individual stock entity The market value of the latest total market capitalization of the total market capitalization: the market value ratio = the latest total market value of the individual entity / the total market value of the industry * 100%; then, based on the market evaluation index of the stock entity at that point in time and the market value of the stock entity The industry evaluation index at this point in time: industry evaluation index = market value of individual stocks * market evaluation index; finally, according to the above method, calculate the industry evaluation index at each time point, and the industry evaluation index at each time point according to time The sequence of industry evaluation indexes corresponding to the stock entity is generated in sequence.

Through the above-mentioned industry evaluation index of individual stock entities, it can be concluded that the industry's evaluation of the stock entity is changed. Through the industry evaluation index sequence of individual stock entities, the changes and trends of the industry's evaluation of the stock entities can be obtained for market analysis. .

In an embodiment, on the basis of the foregoing embodiment, when the processing system is executed by the processor, the following steps are further implemented: adding the industry evaluation indexes of the individual entities belonging to the same industry category at the same time point to obtain The market index of the industry category at the time point; obtaining the market index of the industry category at each time point, and generating the market index sequence corresponding to the industry category in chronological order for the market index at each time point.

Among them, through the market index of the above-mentioned industry categories, it can be concluded that the market's evaluation of the industry changes, and the market index series corresponding to the industry category can be used to derive the changes and trends of the market's evaluation of the industry for market analysis. .

In addition, by summarizing the market indices of all industries at the same time point, the market's emotional expression and views on the entire capital market can be obtained for market analysis.

As shown in FIG. 2, FIG. 2 is a schematic flowchart of an embodiment of a method for processing text data according to the present application. The method for processing text data includes the following steps:

Step S1, dividing various financial text data into corresponding text object types according to a preset classification rule;

The default classification rule is to classify the text object type of the performance-related financial text data as the performance type, the text object type of the financing-related financial text data into the financing type, and the text of the financial text data related to the corporate governance. The object type is classified as the corporate governance type, the text object type of the financial text data related to the analyst is classified as the analyst type, and the text object type of the financial text data other than the above four types is classified into other types, as in Table 1 above. Shown.

In Table 1, the performance types include performance reports, performance reports, and performance reports. The types of financing include: private placements and targeted breaks. Corporate governance types include: executive increase and decrease, major shareholder reduction, equity incentives, and employee holdings. Shares, analyst types include a sharp increase in earnings forecasts, analysts suddenly concerned, other types include: high delivery, index component adjustments, early disclosure of annual reports, long-term announcements.

Step S2, analyzing financial text data of each text object type of each stock entity at each time point by using a predetermined text analysis method, and obtaining an evaluation level corresponding to each financial text data;

Among them, the individual stock entities are listed companies, and each stock entity will generate some financial text data at each time point. The time points can be every minute, every hour, every day, and so on.

In an embodiment, as shown in FIG. 3, the financial text data of each text object type of each stock entity at each time point is analyzed by using a predetermined text analysis method, and the evaluation level corresponding to each financial text data is obtained. The steps include:

Step S21, using a predetermined word segmentation model to segment each financial text data to obtain a word segment corresponding to each financial text data; in step S22, input the word segment corresponding to each financial text data into a predetermined conversion model, and obtain each output of the output. a word vector corresponding to the financial text data; in step S23, the word vector corresponding to each financial text data is input into a predetermined sentiment analysis model, and the sentiment analysis result of each sentence in the output financial text data is obtained; step S24, The sentiment analysis result of each sentence in the financial text data is counted, and the evaluation level corresponding to the financial text data is obtained according to the statistical sentiment analysis result.

Among them, the text of the financial text data is segmented by an already trained word segmentation model, which is a trained neural network segmentation model, preferably a long-term and short-term memory cycle neural network. The process of training the neural network segmentation model includes: 1. Extracting a large number of well-written words from the corpus, wherein the model training uses predetermined segmentation corpora, such as the classic snippet corpus of Microsoft Research in bakeoff2005. 2. Train the training part and use the test part as the final test. 3. By comparing the input and output results of the neural network segmentation model (using the sequence labeling method) to judge the error of the model, if the test effect reaches 0.95 or above, the neural network segmentation model is completed.

Among them, the predetermined conversion model is the word2vec model, and the word2vec model includes a three-layer neural network, which can represent a word as a word vector and digitize the text. The word segment corresponding to the financial text data is input to the word2vec model to obtain a word vector corresponding to the financial text data.

The predetermined sentiment analysis model is a Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts. The main structure of the model is to input a sentence vector corresponding to a sentence text, after two layers. After the Convolutional Neural Network (CNN), it is transformed into a sentence-level vector, and then the vector is input into a 3-layer neural network, and the correct sentiment analysis result of the sentence is obtained through training.

In an embodiment, the sentiment analysis result includes three types, for example: [-1, 0, 1], wherein -1 indicates that the emotion expressed by the sentence is negative and negative, and 0 indicates that the emotion expressed by the sentence is biased. Neutral, 1 means that the expression expressed in the sentence is positive.

In addition, the output dimension of the 3-layer neural network can be adjusted by itself, which can be the above three-dimensional [-1, 0, 1], or two-dimensional [-1, 1], and its value is from -1 to 1. The preference for 1 means that the expression of the sentence is positive, and the bias of -1 means that the emotion expressed by the sentence is negative, negative, and so on.

The evaluation level of the financial text data includes the first level, the second level, and the third level, and the evaluation level may also be a good rating, a middle rating, and a bad rating. The first level corresponds to the above-described sentiment analysis result 1, and the second level corresponds to the above-described sentiment analysis result 0, corresponding to the above-described sentiment analysis result-1. Finally, the output of all sentences is fused together to calculate the total number of sentiment analysis results. If the total number of sentiment analysis results is the largest, the financial text data is the first level, and if the total number of sentiment analysis results is 0. At most, the financial text data is in the second level, and if the total number of sentiment analysis results is -1, the financial text data is in the third level.

Step S3, the number of each evaluation level of the financial text data under each text object type is counted, and the proportion of each evaluation level is calculated based on the number of each evaluation level after the statistics;

The statistics of the number of evaluation levels of the financial text data under each text object type include: counting the number of the first level, the second level, and the third level of the financial text data under each text object type, The data of company A is taken as an example, as shown in Table 2 above.

In Table 2, the number of the first level, the second level, and the third level are 10, 5, and 4, respectively, and the total number of evaluations is 10 + 5 + 4 = 19, and the specific gravity of the first level = 10 / 19 * 100 %=52.63%, the specific gravity of the second grade=5/19*100%=26.32%, and the specific gravity of the third grade=4/19*100%=21.05%.

In step S4, attribute scores of each evaluation level are obtained, and the market evaluation index corresponding to the stock entity at the time point is calculated according to the attribute score of each evaluation level and the proportion of each evaluation level.

The first level has an attribute score of 1, the second level has an attribute score of 0, the third level has an attribute score of -1, and the market evaluation index = 100* [the first level of the proportion *1 + second The proportion of the level *0 + the proportion of the third level * (-1)]. Taking the above company A as an example, the market evaluation index=100*(52.63%*1+26.32%*0+21.05%*(-1))=31.58. According to the market evaluation index, the market's evaluation of the company at that point in time can be obtained for market analysis.

Step S5: Obtain a market evaluation index of the stock entity at each time point, and generate a market evaluation index sequence corresponding to the stock entity in a chronological order for the market evaluation index at each time point.

In this embodiment, according to the chronological order, a corresponding market evaluation index sequence is generated according to the market evaluation index of the above-mentioned individual entity, and a market evaluation index sequence of the company to which the individual entity belongs is obtained, and according to the market evaluation index sequence, the market pair can be obtained. The company's evaluation of changes and trends for market analysis.

The application divides the financial text data into different text object types and analyzes by using a predetermined text analysis method, and can fully extract accurate market information, and generate a market evaluation index sequence in time sequence, which can be obtained from the market. Changes and trends in the company's evaluation for market analysis.

In an embodiment, as shown in FIG. 4, on the basis of the foregoing embodiment, the processing method of the text data further includes:

Step S6, according to a predetermined industry classification method, each individual entity is divided into corresponding industry categories, obtaining the latest total market value of each individual entity, and calculating the total market value corresponding to each industry category according to the latest total market value of each individual entity; step S7, according to Calculating the market value of the entity by calculating the latest total market value of each individual entity and the total market value corresponding to the industry category to which the entity belongs; step S8, calculating the entity according to the market evaluation index and the market value of the stock entity at the time point At the point of time, the industry evaluation index; step S9, obtaining the industry evaluation index of the stock entity at each time point, and generating the industry evaluation index sequence corresponding to the stock entity in the chronological order of the industry evaluation index at each time point.

Among them, the predetermined industry classification method is, for example, the Shenwan industry classification method. In an embodiment, all the entities in the Shanghai and Shenzhen stock exchanges can be divided into the following 28 industry categories, including: mining, chemical, steel, non-ferrous metals, building materials, architectural decoration, electrical equipment, mechanical equipment, national defense military, automobile , household appliances, textile and garment, light industry manufacturing, commercial trade, agriculture, forestry, animal husbandry and fishery, food and beverage, leisure services, medical and biological, public utilities, transportation, real estate, electronics, computers, media, communications, banking, non-banking finance, comprehensive .

Taking the industry category of the bank as an example, calculating the bank's industry evaluation index includes: first, extracting the latest total market value of each stock entity, adding the latest total market value of each stock entity to the total market value of the industry; second, calculating the individual stock entity The market value of the latest total market capitalization of the total market capitalization: the market value ratio = the latest total market value of the individual entity / the total market value of the industry * 100%; then, based on the market evaluation index of the stock entity at that point in time and the market value of the stock entity The industry evaluation index at this point in time: industry evaluation index = market value of individual stocks * market evaluation index; finally, according to the above method, calculate the industry evaluation index at each time point, and the industry evaluation index at each time point according to time The sequence of industry evaluation indexes corresponding to the stock entity is generated in sequence.

Through the above-mentioned industry evaluation index of individual stock entities, it can be concluded that the industry's evaluation of the stock entity is changed. Through the industry evaluation index sequence of individual stock entities, the changes and trends of the industry's evaluation of the stock entities can be obtained for market analysis. .

In an embodiment, on the basis of the foregoing embodiment, the method for processing the text data further includes: adding the industry evaluation indexes of the individual entities belonging to the same industry category at the same time point to obtain the industry category at the time point. Market index; obtain the market index of the industry category at each time point, and generate the market index sequence corresponding to the industry category in chronological order for the market index at each time point.

Among them, through the market index of the above-mentioned industry categories, it can be concluded that the market's evaluation of the industry changes, and the market index series corresponding to the industry category can be used to derive the changes and trends of the market's evaluation of the industry for market analysis. .

In addition, by summarizing the market indices of all industries at the same time point, the market's emotional expression and views on the entire capital market can be obtained for market analysis.

The present application also provides a computer readable storage medium having stored thereon a processing system, the processing system being implemented by a processor to implement the steps of the text data processing method described above.

The serial numbers of the embodiments of the present application are merely for the description, and do not represent the advantages and disadvantages of the embodiments.

Through the description of the above embodiments, those skilled in the art can clearly understand that the foregoing embodiment method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be through hardware, but in many cases, the former is better. Implementation. Based on such understanding, the technical solution of the present application, which is essential or contributes to the prior art, may be embodied in the form of a software product stored in a storage medium (such as ROM/RAM, disk, The optical disc includes a number of instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the methods described in various embodiments of the present application.

The above is only a preferred embodiment of the present application, and is not intended to limit the scope of the patent application, and the equivalent structure or equivalent process transformations made by the specification and the drawings of the present application, or directly or indirectly applied to other related technical fields. The same is included in the scope of patent protection of this application.

Claims (20)

1. A server, comprising: a memory and a processor coupled to the memory, the memory storing a processing system operable on the processor, the processing system being The following steps are implemented during execution:

   According to a preset classification rule, various financial text data are classified into corresponding text object types, wherein the text object types include a performance type, a financing type, a corporate governance type, an analyst type, and other types;

   Using a predetermined text analysis method, analyzing financial text data of each text object type of each stock entity at each time point, and obtaining an evaluation level corresponding to each financial text data;

   The number of each evaluation level of the financial text data under each text object type is counted, and the proportion of each evaluation level is calculated based on the number of each evaluation level after the statistics;

   Obtaining an attribute score corresponding to each evaluation level, and calculating a market evaluation index of the stock entity at the time point according to the attribute score corresponding to each evaluation level and the proportion of each evaluation level;

   Obtain the market evaluation index of the stock entity at each time point, and generate the market evaluation index sequence corresponding to the stock entity in the chronological order of the market evaluation index at each time point.
2. The server according to claim 1, wherein said evaluation level comprises a first level, a second level, and a third level, said first level having an attribute score of 1, said second level of attribute points The value is 0, the attribute score of the third level is -1, the market evaluation index = 100* [the proportion of the first level * 1 + the proportion of the second level * 0 + the proportion of the third level * (- 1)].
3. The server according to claim 1, wherein said using a predetermined text analysis method analyzes financial text data of each text object type of each stock entity at each time point, and obtains an evaluation corresponding to each financial text data. The steps of the level include:

   Each financial text data is segmented by a predetermined word segmentation model to obtain a word segment corresponding to each financial text data;

   Entering a word segment corresponding to each financial text data into a predetermined conversion model, and obtaining a word vector corresponding to each financial text data outputted;

   Inputting a word vector corresponding to each financial text data into a predetermined sentiment analysis model, and obtaining an sentiment analysis result of each sentence in the output financial text data;

   The sentiment analysis result of each sentence in the financial text data is counted, and the evaluation level corresponding to the financial text data is obtained according to the statistical sentiment analysis result.
4. The server according to claim 2, wherein said analyzing the financial text data of each text object type of each stock entity at each time point by using a predetermined text analysis method, and obtaining corresponding evaluation of each financial text data The steps of the level include:

   Each financial text data is segmented by a predetermined word segmentation model to obtain a word segment corresponding to each financial text data;

   Entering a word segment corresponding to each financial text data into a predetermined conversion model, and obtaining a word vector corresponding to each financial text data outputted;

   Inputting a word vector corresponding to each financial text data into a predetermined sentiment analysis model, and obtaining an sentiment analysis result of each sentence in the output financial text data;

   The sentiment analysis result of each sentence in the financial text data is counted, and the evaluation level corresponding to the financial text data is obtained according to the statistical sentiment analysis result.
5. The server according to claim 1, wherein when said processing system is executed by said processor, the following steps are further implemented:

   According to the predetermined industry classification method, each individual entity is divided into corresponding industry categories, the latest total market value of each individual entity is obtained, and the total market value corresponding to each industry category is calculated according to the latest total market value of each individual entity;

   Calculating the market value of the entity based on the latest total market value of each individual entity and the total market value corresponding to the industry category to which the entity belongs;

   Calculating the industry evaluation index of the stock entity at the time point according to the market evaluation index of the stock entity at the time point and the market value ratio;

   Obtain the industry evaluation index of the stock entity at each time point, and generate the industry evaluation index sequence corresponding to the stock entity in the chronological order of the industry evaluation index at each time point.
6. The server according to claim 2, wherein when said processing system is executed by said processor, the following steps are further implemented:

   According to the predetermined industry classification method, each individual entity is divided into corresponding industry categories, the latest total market value of each individual entity is obtained, and the total market value corresponding to each industry category is calculated according to the latest total market value of each individual entity;

   Calculating the market value of the entity based on the latest total market value of each individual entity and the total market value corresponding to the industry category to which the entity belongs;

   Calculating the industry evaluation index of the stock entity at the time point according to the market evaluation index of the stock entity at the time point and the market value ratio;

   Obtain the industry evaluation index of the stock entity at each time point, and generate the industry evaluation index sequence corresponding to the stock entity in the chronological order of the industry evaluation index at each time point.
7. A method for processing text data, characterized in that the processing method of the text data comprises:

   S1, classifying various financial text data into corresponding text object types according to a preset classification rule, wherein the text object types include a performance type, a financing type, a corporate governance type, an analyst type, and other types;

   S2, analyzing financial text data of each text object type of each stock entity at each time point by using a predetermined text analysis method, and obtaining an evaluation level corresponding to each financial text data;

   S3, the number of each evaluation level of the financial text data under each text object type is counted, and the proportion of each evaluation level is calculated based on the number of each evaluation level after the statistics;

   S4, obtaining an attribute score corresponding to each evaluation level, and calculating a market evaluation index of the stock entity at the time point according to the attribute score corresponding to each evaluation level and the proportion of each evaluation level;

   S5: Obtain a market evaluation index of the stock entity at each time point, and generate a market evaluation index sequence corresponding to the stock entity in a chronological order by the market evaluation index at each time point.
8. The method of processing text data according to claim 7, wherein the evaluation level comprises a first level, a second level, and a third level, and the attribute level of the first level is 1, the second The attribute score of the level is 0, the attribute score of the third level is -1, the market evaluation index = 100* [the proportion of the first level * 1 + the proportion of the second level * 0 + the third level Specific gravity * (-1)].
9. The method of processing text data according to claim 7, wherein the step S2 comprises:

   Each financial text data is segmented by a predetermined word segmentation model to obtain a word segment corresponding to each financial text data;

   Entering a word segment corresponding to each financial text data into a predetermined conversion model, and obtaining a word vector corresponding to each financial text data outputted;

   Inputting a word vector corresponding to each financial text data into a predetermined sentiment analysis model, and obtaining an sentiment analysis result of each sentence in the output financial text data;

   The sentiment analysis result of each sentence in the financial text data is counted, and the evaluation level corresponding to the financial text data is obtained according to the statistical sentiment analysis result.
10. The method of processing text data according to claim 8, wherein the step S2 comprises:

    Each financial text data is segmented by a predetermined word segmentation model to obtain a word segment corresponding to each financial text data;

    Entering a word segment corresponding to each financial text data into a predetermined conversion model, and obtaining a word vector corresponding to each financial text data outputted;

    Inputting a word vector corresponding to each financial text data into a predetermined sentiment analysis model, and obtaining an sentiment analysis result of each sentence in the output financial text data;

    The sentiment analysis result of each sentence in the financial text data is counted, and the evaluation level corresponding to the financial text data is obtained according to the statistical sentiment analysis result.
11. The method for processing text data according to claim 7, wherein the processing method of the text data further comprises:

    According to the predetermined industry classification method, each individual entity is divided into corresponding industry categories, the latest total market value of each individual entity is obtained, and the total market value corresponding to each industry category is calculated according to the latest total market value of each individual entity;

    Calculating the market value of the entity based on the latest total market value of each individual entity and the total market value corresponding to the industry category to which the entity belongs;

    After the step S4, the method further includes:

    Calculating the industry evaluation index of the stock entity at the time point according to the market evaluation index of the stock entity at the time point and the market value ratio;

    Obtain the industry evaluation index of the stock entity at each time point, and generate the industry evaluation index sequence corresponding to the stock entity in the chronological order of the industry evaluation index at each time point.
12. The method for processing text data according to claim 8, wherein the processing method of the text data further comprises:

    According to the predetermined industry classification method, each individual entity is divided into corresponding industry categories, the latest total market value of each individual entity is obtained, and the total market value corresponding to each industry category is calculated according to the latest total market value of each individual entity;

    Calculating the market value of the entity based on the latest total market value of each individual entity and the total market value corresponding to the industry category to which the entity belongs;

    After the step S4, the method further includes:

    Calculating the industry evaluation index of the stock entity at the time point according to the market evaluation index of the stock entity at the time point and the market value ratio;

    Obtain the industry evaluation index of the stock entity at each time point, and generate the industry evaluation index sequence corresponding to the stock entity in the chronological order of the industry evaluation index at each time point.
13. The method for processing text data according to claim 11 or 12, wherein the method for processing the text data further comprises:

    Adding the industry evaluation indexes of the individual entities belonging to the same industry category at the same time point to obtain the market index of the industry category at that point in time;

    Obtain a market index of the industry category at each time point, and generate a market index sequence corresponding to the industry category in a chronological order for the market index at each time point.
14. A computer readable storage medium, wherein the computer readable storage medium stores a processing system, and when the processing system is executed by the processor, the steps are:

    According to a preset classification rule, various financial text data are classified into corresponding text object types, wherein the text object types include a performance type, a financing type, a corporate governance type, an analyst type, and other types;

    Using a predetermined text analysis method, analyzing financial text data of each text object type of each stock entity at each time point, and obtaining an evaluation level corresponding to each financial text data;

    The number of each evaluation level of the financial text data under each text object type is counted, and the proportion of each evaluation level is calculated based on the number of each evaluation level after the statistics;

    Obtaining an attribute score corresponding to each evaluation level, and calculating a market evaluation index of the stock entity at the time point according to the attribute score corresponding to each evaluation level and the proportion of each evaluation level;

    Obtain the market evaluation index of the stock entity at each time point, and generate the market evaluation index sequence corresponding to the stock entity in the chronological order of the market evaluation index at each time point.
15. The computer readable storage medium of claim 14, wherein the rating level comprises a first level, a second level, and a third level, the first level having an attribute score of 1, the second The attribute score of the level is 0, the attribute score of the third level is -1, the market evaluation index = 100* [the proportion of the first level * 1 + the proportion of the second level * 0 + the third level Specific gravity * (-1)].
16. The computer readable storage medium according to claim 14, wherein said analyzing the financial text data of each text object type of each stock entity at each time point by using a predetermined text analysis method, and obtaining each financial text The steps of the evaluation level corresponding to the data specifically include:

    Each financial text data is segmented by a predetermined word segmentation model to obtain a word segment corresponding to each financial text data;

    Entering a word segment corresponding to each financial text data into a predetermined conversion model, and obtaining a word vector corresponding to each financial text data outputted;

    Inputting a word vector corresponding to each financial text data into a predetermined sentiment analysis model, and obtaining an sentiment analysis result of each sentence in the output financial text data;

    The sentiment analysis result of each sentence in the financial text data is counted, and the evaluation level corresponding to the financial text data is obtained according to the statistical sentiment analysis result.
17. The computer readable storage medium according to claim 15, wherein said analyzing the financial text data of each text object type of each stock entity at each time point by using a predetermined text analysis method, and obtaining each financial text The steps of the evaluation level corresponding to the data specifically include:

    Each financial text data is segmented by a predetermined word segmentation model to obtain a word segment corresponding to each financial text data;

    Entering a word segment corresponding to each financial text data into a predetermined conversion model, and obtaining a word vector corresponding to each financial text data outputted;

    Inputting a word vector corresponding to each financial text data into a predetermined sentiment analysis model, and obtaining an sentiment analysis result of each sentence in the output financial text data;

    The sentiment analysis result of each sentence in the financial text data is counted, and the evaluation level corresponding to the financial text data is obtained according to the statistical sentiment analysis result.
18. The computer readable storage medium of claim 14, wherein when the processing system is executed by the processor, the following steps are further implemented:

    According to the predetermined industry classification method, each individual entity is divided into corresponding industry categories, the latest total market value of each individual entity is obtained, and the total market value corresponding to each industry category is calculated according to the latest total market value of each individual entity;

    Calculating the market value of the entity based on the latest total market value of each individual entity and the total market value corresponding to the industry category to which the entity belongs;

    Calculating the industry evaluation index of the stock entity at the time point according to the market evaluation index of the stock entity at the time point and the market value ratio;

    Obtain the industry evaluation index of the stock entity at each time point, and generate the industry evaluation index sequence corresponding to the stock entity in the chronological order of the industry evaluation index at each time point.
19. The computer readable storage medium of claim 15, wherein when the processing system is executed by the processor, the following steps are further implemented:

    According to the predetermined industry classification method, each individual entity is divided into corresponding industry categories, the latest total market value of each individual entity is obtained, and the total market value corresponding to each industry category is calculated according to the latest total market value of each individual entity;

    Calculating the market value of the entity based on the latest total market value of each individual entity and the total market value corresponding to the industry category to which the entity belongs;

    Calculating the industry evaluation index of the stock entity at the time point according to the market evaluation index of the stock entity at the time point and the market value ratio;

    Obtain the industry evaluation index of the stock entity at each time point, and generate the industry evaluation index sequence corresponding to the stock entity in the chronological order of the industry evaluation index at each time point.
20. A computer readable storage medium according to claim 18 or claim 19, wherein when said processing system is executed by said processor, the step of: realizing individual entities belonging to the same industry category at the same time point in the industry The evaluation index is added to obtain the market index of the industry category at the time point; obtaining the market index of the industry category at each time point, and generating the market index sequence corresponding to the industry category in chronological order for the market index at each time point .

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