CN116228302A - Analysis method and device of business market environment, processor and electronic equipment - Google Patents
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Abstract
The application discloses a business market environment analysis method, a business market environment analysis device, a processor and electronic equipment. Relates to the field of financial science and technology, and the method comprises the following steps: acquiring business information data in a target time period from a business information website of a target business, and screening text data from the business information data; inputting text data into a bidirectional coding model, and outputting emotion reasoning results of a transactor for processing target business, wherein the bidirectional coding model is obtained by training a preset bidirectional coding model by M groups of sample data, and the M groups of sample data comprise text samples and emotion data of the text samples; and determining market environment information of the target service according to the emotion reasoning result. By the method and the device, the problem that the investor emotion is difficult to accurately acquire in the related technology and the market environment is effectively identified by utilizing the investor emotion is solved.
Description
Technical Field
The present application relates to the field of financial science and technology, and in particular, to a method, an apparatus, a processor, and an electronic device for analyzing a business market environment.
Background
With the development of the internet, internet-based stock market trading is the mainstream, and mechanism adjustment research on markets based on the emotion of investors is more and more popular in the field of finance.
In the traditional technology, emotion research is usually carried out by using a survey method, but the method needs to waste a great deal of time cost and labor cost, and is extremely easy to cause deviation due to the limited number of samples; therefore, proxy analysis is adopted for the emotion of the investor by selecting variables such as closed foundation discount rate, turnover rate and the like and using a principal component analysis method; this method is widely used because it is easy to implement. However, the method can only indirectly reflect the emotion of investors, and has poor research effect.
Aiming at the problems that the emotion of an investor is difficult to accurately acquire and the market environment is effectively identified by utilizing the emotion of the investor in the related technology, no effective solution is proposed at present.
Disclosure of Invention
The main purpose of the application is to provide an analysis method, an analysis device, an analysis processor and an analysis electronic device for a business market environment, so as to solve the problems that investors 'moods are difficult to accurately acquire and effectively identify the market environment by utilizing the investors' moods in the related technology.
In order to achieve the above object, according to one aspect of the present application, there is provided a method of analyzing a business market environment. The method comprises the following steps: acquiring business information data in a target time period from a business information website of a target business, and screening text data from the business information data; inputting text data into a bidirectional coding model, and outputting emotion reasoning results of a transactor for processing target business, wherein the bidirectional coding model is obtained by training a preset bidirectional coding model by M groups of sample data, and the M groups of sample data comprise text samples and emotion data of the text samples; and determining market environment information of the target service according to the emotion reasoning result.
Optionally, the screening text data from the business information data includes: acquiring a preset deleting program and a data cleaning rule, wherein the data cleaning rule indicates the type of data to be deleted, the type of the data to be deleted comprises repeated text data, form data and tag data, and the deleting program is used for deleting the data to be deleted; screening the business information data based on the data cleaning rule by using a deleting program to obtain N initial text data; and processing the N initial text data to obtain the text data.
Optionally, processing the N initial text data to obtain text data includes: calculating an influence degree parameter of each piece of initial text data, wherein the influence degree parameter refers to an influence degree value of the initial text data on a transactor; and performing descending order arrangement on the N initial text data according to the influence degree parameters to obtain an initial text data sequence, and determining the initial text data with the preset quantity in the initial text data sequence as text data.
Optionally, the bi-directional coding model is trained by: forming a group of first sample data by M1 first text samples and emotion data of each first text sample to obtain M1 groups of first sample data; training a preset bidirectional coding model through M1 groups of first sample data to obtain an initial bidirectional coding model; forming a group of second sample data by M2 second text samples and emotion data of each second text sample to obtain M2 groups of second sample data, wherein the sum of M1 and M2 is M, and the emotion value of the emotion data of the second sample data is larger than a preset emotion value; and training an initial bidirectional coding model through M2 groups of second sample data to obtain a bidirectional coding model.
Optionally, before determining the market environment information of the target service according to the emotion inference result, the method further comprises: selecting trade index data from the business information data, and calculating according to the trade index data to obtain market environment data; inputting the text data and the market environment data into a time-varying parameter vector autoregressive model, and outputting the emotion reasoning result of the trader and the relativity of the market environment data; and under the condition that the relevance is greater than a relevance threshold, determining market environment information of the target service according to the emotion reasoning result.
Optionally, the emotion inference result includes a positive emotion inference result and a negative emotion inference result, and determining market environment information of the target service according to the emotion inference result includes: carrying out weighted summation calculation on forward emotion reasoning results corresponding to each text data in a target time period to obtain a first group of emotion indexes; carrying out weighted summation calculation on negative emotion reasoning results corresponding to each text data in a target time period to obtain a second group of emotion indexes; and calculating a total emotion index in the target time period by using the first group of emotion indexes and the second group of emotion indexes, and determining market environment information of the business according to the total emotion index.
Optionally, determining the market environment information of the target business according to the emotion inference result includes: under the condition that the total emotion index is larger than a preset emotion index value, determining market environment information as a first type environment; and under the condition that the total emotion index is smaller than the preset emotion index value, determining the market environment information as a second type environment.
In order to achieve the above object, according to another aspect of the present application, there is provided an analysis apparatus of a business market environment. The device comprises: the acquisition unit is used for acquiring service information data in a target time period from a service information website of a target service and screening text data from the service information data; the first input unit is used for inputting text data into a bi-directional coding model and outputting emotion reasoning results of a transactor of a processing target service, wherein the bi-directional coding model is obtained by training a preset bi-directional coding model through M groups of sample data, and the M groups of sample data comprise text samples and emotion data of the text samples; and the first determining unit is used for determining market environment information of the target service according to the emotion reasoning result.
According to another aspect of the embodiment of the present invention, there is further provided a processor, configured to execute a program, where the program when executed controls a device in which a nonvolatile storage medium is located to execute a method for analyzing a business market environment.
According to another aspect of embodiments of the present invention, there is also provided an electronic device including one or more processors and a memory; the memory has stored therein computer readable instructions, and the processor is configured to execute the computer readable instructions, wherein the computer readable instructions when executed perform a method of analyzing a business market environment.
Through the application, the following steps are adopted: acquiring business information data in a target time period from a business information website of a target business, and screening text data from the business information data; inputting text data into a bidirectional coding model, and outputting emotion reasoning results of a transactor for processing target business, wherein the bidirectional coding model is obtained by training a preset bidirectional coding model by M groups of sample data, and the M groups of sample data comprise text samples and emotion data of the text samples; the method comprises the steps of determining market environment information of a target service according to emotion reasoning results, solving the problems that investors are difficult to accurately acquire emotion in related technologies and effectively identify market environment by utilizing the investors, carrying out emotion classification of text data by utilizing a trained bidirectional coding model to obtain emotion reasoning results of traders, determining market environment information according to the emotion reasoning results, and further achieving the effect of accurately determining the market environment information.
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The accompanying drawings, which are included to provide a further understanding of the application, illustrate and explain the application and are not to be construed as limiting the application. In the drawings:
FIG. 1 is a flow chart of a method of analysis of a business market environment provided in accordance with an embodiment of the present application;
FIG. 2 is a schematic diagram of a predictive emotional inference result of a transactor via a bi-directional coding model provided in accordance with an embodiment of the present application;
FIG. 3 is a schematic diagram of word vector conversion by bi-directional coding model provided in accordance with an embodiment of the present application;
FIG. 4 is a schematic diagram of converting word vectors into emotion data through a bi-directional coding model, provided in accordance with an embodiment of the present application;
FIG. 5 is a schematic diagram of an investor mood profile based on time periods provided in accordance with an embodiment of the present application;
FIG. 6 is a schematic diagram of an analysis device for a business market environment provided in accordance with an embodiment of the present application;
fig. 7 is a schematic diagram of an electronic device provided according to an embodiment of the present application.
Detailed Description
It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
In order to make the present application solution better understood by those skilled in the art, the following description will be made in detail and with reference to the accompanying drawings in the embodiments of the present application, it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate in order to describe the embodiments of the present application described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that, related information (including, but not limited to, user equipment information, user personal information, etc.) and data (including, but not limited to, data for presentation, analyzed data, etc.) related to the present disclosure are information and data authorized by a user or sufficiently authorized by each party.
The invention will be described with reference to preferred implementation steps, and fig. 1 is a flowchart of a method for analyzing a business market environment according to an embodiment of the present application, as shown in fig. 1, and the method includes the following steps:
step S101, service information data in a target time period is obtained from a service information website of a target service, and text data is screened from the service information data.
Specifically, the target business may refer to a business for stock exchange, and in the case that the target business is a stock exchange business, the business information website may include a plurality of financial websites for providing global financial information and quotes such as securities, bonds, futures, etc. in the global financial market; the business information data may include comment data of stock market quotations on a plurality of financial websites and a plurality of index data issued by authorities.
It should be noted that, the business information data includes multiple types of data, and in order to accurately determine the emotion inference result of the transactor through the business information data, the data such as a table in the business information needs to be deleted to obtain the data expressed in a literal manner, that is, text data.
The target time period may be a component time period in units of weeks, that is, the business information data may be various comment data and index data within one week acquired from the business information website using a tool. Specifically, a plurality of business information data can be obtained from the information websites corresponding to the securities trade markets by using an open-source application programming interface data, namely API (Application Program Interface) and a screening script, and text data can be obtained by screening from the business information data, so that a data foundation is laid for accurately determining the emotion reasoning results of traders.
Step S102, inputting text data into a bi-directional coding model, and outputting emotion reasoning results of a transactor of a processing target service, wherein the bi-directional coding model is obtained by training a preset bi-directional coding model by M groups of sample data, and the M groups of sample data comprise text samples and emotion data of the text samples.
It should be noted that, the bi-directional coding model is named Bidirectional Encoder Representation from Transformers, abbreviated as BERT model, and the trader refers to a practitioner who performs a stock exchange business, i.e. an investor.
Fig. 2 is a schematic diagram of predicting an emotion inference result of a transactor through a bi-directional coding model according to an embodiment of the present application, as shown in fig. 2, specifically, text data obtained through screening processing is input into a trained bi-directional coding model, and emotion data of an investor, that is, an emotion inference result, can be obtained through processing of the bi-directional coding model, which refers to an emotion state held when the investor obtains the text data and faces a financial investment market within a target time period, where the emotion inference result includes two results of positive emotion and negative emotion.
Fig. 3 is a schematic diagram of word vector conversion by a bi-directional coding model according to an embodiment of the present application, as shown in fig. 3, E1, E2.
Fig. 4 is a schematic diagram of converting word vectors into emotion data through a bi-directional coding model according to an embodiment of the present application, as shown in fig. 4, the word vectors are input into a preset bi-directional coding model, three embedding modes of word embedding, segment embedding and position embedding and masking operation are respectively performed, and emotion data corresponding to text data, that is, emotion reasoning results of a transactor, are output.
Optionally, in the method for analyzing a business market environment provided by the embodiment of the present application, the bidirectional coding model is obtained by training in the following manner: forming a group of first sample data by M1 first text samples and emotion data of each first text sample to obtain M1 groups of first sample data; training a preset bidirectional coding model through M1 groups of first sample data to obtain an initial bidirectional coding model; forming a group of second sample data by M2 second text samples and emotion data of each second text sample to obtain M2 groups of second sample data, wherein the sum of M1 and M2 is M, and the emotion value of the emotion data of the second sample data is larger than a preset emotion value; and training an initial bidirectional coding model through M2 groups of second sample data to obtain a bidirectional coding model.
It should be noted that the bi-directional coding model is trained from M sets of sample data. Specifically, the M groups of sample data are divided into M1 groups of first sample data and M2 groups of second sample data, the model is pre-trained through the first sample data, the model is secondarily trained through the second sample data, the sample data during pre-training and secondary training are equally divided into a training set and a testing set, and the data proportion of the training set and the testing set can be 8:2.
the first sample data can be data obtained from a corpus in the unlabeled field, first, a pre-training stage is carried out on a preset bidirectional coding model by a first text sample and corresponding emotion data in the first sample data, an initial bidirectional coding model is obtained through the pre-training stage, and the initial bidirectional coding model can carry out initial emotion recognition on different financial data, but the accuracy of the recognized result is lower and further training is needed.
The second sample data can be data obtained from a corpus in the financial field, the second sample data has clear emotion characteristics, further, the second text sample in the second sample data and the corresponding emotion data are used for carrying out secondary training on the initial bi-directional coding model, and the identification of the emotion data of the financial text can be realized through the initial bi-directional coding model of the secondary training, so that the accuracy of the emotion reasoning result of the output transactor is greatly improved, and higher accuracy is achieved.
And step S103, determining market environment information of the target business according to the emotion reasoning result.
Specifically, the market environment information may include a positive environment and a more negative environment, and since the emotion condition of the investor has a great influence on the financial market, the market environment information in the current target time period can be determined through the emotion reasoning result output by the model, and further, different angles of adjustment are performed for the market environment, for example, when the market environment information is displayed as the negative environment, targeted policy reform needs to be performed and financial transaction is promoted for the investor; when the market environment information is displayed as a positive environment, a persistent stable market environment is required, and thus financial transactions are further encouraged, advancing the financial market toward a steady trend.
According to the analysis method of the business market environment, business information data in a target time period are obtained from a business information website of a target business, and text data are screened from the business information data; inputting text data into a bidirectional coding model, and outputting emotion reasoning results of a transactor for processing target business, wherein the bidirectional coding model is obtained by training a preset bidirectional coding model by M groups of sample data, and the M groups of sample data comprise text samples and emotion data of the text samples; the method comprises the steps of determining market environment information of a target service according to emotion reasoning results, solving the problems that investors are difficult to accurately acquire emotion in related technologies and effectively identify market environment by utilizing the investors, carrying out emotion classification of text data by utilizing a trained bidirectional coding model to obtain emotion reasoning results of traders, determining market environment information according to the emotion reasoning results, and further achieving the effect of accurately determining the market environment information.
The text data is obtained by screening by a deleting program, and optionally, in the analysis method of the business market environment provided by the embodiment of the application, the screening of the text data from the business information data includes: acquiring a preset deleting program and a data cleaning rule, wherein the data cleaning rule indicates the type of data to be deleted, the type of the data to be deleted comprises repeated text data, form data and tag data, and the deleting program is used for deleting the data to be deleted; screening the business information data based on the data cleaning rule by using a deleting program to obtain N initial text data; and processing the N initial text data to obtain the text data.
Specifically, the preset deleting program is a program for compiling a data cleaning rule into the preset program, the data cleaning rule stores text data types to be deleted, and the compiled program is utilized to perform target data screening operation from the acquired multiple service information data, so as to obtain multiple initial text data.
For example, the acquired business information data comprises a plurality of text comment data and a plurality of form data, the plurality of business information data is subjected to traversal screening by using a deleting program, the form data is deleted, the plurality of text comment data are determined to be initial text data, and the data input into the bi-directional coding model are required to be vectors converted from the text data because the bi-directional coding model is used for determining semantic information contained in the text, so that the effect of accurately determining emotion reasoning results corresponding to the text data by using the model is realized.
Optionally, in the method for analyzing a business market environment provided in the embodiment of the present application, processing N pieces of initial text data to obtain text data includes: calculating an influence degree parameter of each piece of initial text data, wherein the influence degree parameter refers to an influence degree value of the initial text data on a transactor; and performing descending order arrangement on the N initial text data according to the influence degree parameters to obtain an initial text data sequence, and determining the initial text data with the preset quantity in the initial text data sequence as text data.
Since text data includes lengthy comment data such as news stories and analysis reports, such data has a great influence on financial transactions such as investors' stocks, it is necessary to process the potentially influencing data, and in particular, to process the initial text data using a statistical method.
Firstly, obtaining influence degree parameters of each initial text data on investors by using a statistical method, and then ordering the corresponding initial text data in a descending order according to the numerical values of a plurality of influence degree parameters to obtain a data sequence; and finally, selecting a preset number of initial text data from the initial position in the sequence, taking the initial text data as the text data needing to be input into the bidirectional coding model, and arranging the influence degree of the text data to realize the effect of acquiring a plurality of text data with obvious characteristics.
The correlation between the emotion reasoning result and the market environment information can be obtained through the time-varying parameter vector autoregressive model, optionally, in the analysis method of the business market environment provided by the embodiment of the application, before the market environment information of the target business is determined according to the emotion reasoning result, the method further comprises the following steps: selecting trade index data from the business information data, and calculating according to the trade index data to obtain market environment data; inputting the text data and the market environment data into a time-varying parameter vector autoregressive model, and outputting the emotion reasoning result of the trader and the relativity of the market environment data; and under the condition that the relevance is greater than a relevance threshold, determining market environment information of the target service according to the emotion reasoning result.
In order to obtain the correlation between emotion and market environment data, a time-varying parameter vector autoregressive model is needed, specifically, the trade index data can comprise market values, circulation market values and the like obtained from information in multiple aspects such as news, the market environment data can comprise data such as turnover rate, fluctuation rate and the like obtained by calculation through the trade index data, the time-varying parameter vector autoregressive model, namely a TVP-VAR model (Time Varying Parameter-Stochastic Volatility-Vector Auto Regression), is used for representing the dynamic variation relationship among variables, and the correlation between emotion conditions of investors and market fluctuation and mobility, namely the correlation can be obtained through inputting the market environment data and text data into the time-varying parameter vector autoregressive model, and further the financial market is analyzed according to the correlation.
Furthermore, the relevance threshold value refers to a minimum value capable of determining a market environment according to a emotion inference result, and under the condition that the relevance is larger than the relevance threshold value, the situation of the market environment in the current time period can be determined according to the emotion inference result, and the improved market environment can be optimized by utilizing the result output by the bidirectional coding model, so that the effect of accurately judging the market environment by utilizing the relevance threshold value is greatly achieved.
Optionally, in the method for analyzing a business market environment provided by the embodiment of the present application, the emotion inference result includes a positive emotion inference result and a negative emotion inference result, and determining the market environment information of the target business according to the emotion inference result includes: carrying out weighted summation calculation on forward emotion reasoning results corresponding to each text data in a target time period to obtain a first group of emotion indexes; carrying out weighted summation calculation on negative emotion reasoning results corresponding to each text data in a target time period to obtain a second group of emotion indexes; and calculating a total emotion index in the target time period by using the first group of emotion indexes and the second group of emotion indexes, and determining market environment information of the business according to the total emotion index.
The emotion reasoning results obtained through the bidirectional coding model can quantify market environment information, specifically, the emotion reasoning results obtained through outputting each text data in the current target time period are divided into two groups, one group is positive emotion, the other group is negative emotion, and the emotion reasoning results of each group are subjected to weighted summation calculation to obtain emotion indexes, wherein the influence degree of each reasoning result on the market environment information is the same, and therefore the weight value of each result is set to be 1.
After the emotional indexes of the positive emotion and the negative emotion are obtained, the total emotional index is calculated by the following formula:
wherein, send is the total emotion index,
is an emotion index of positive emotion->
The market environment can be analyzed by the total emotion index, which is an emotion index of negative emotion. />
Optionally, in the method for analyzing a business market environment provided in the embodiment of the present application, determining market environment information of a target business according to a result of emotion reasoning includes: under the condition that the total emotion index is larger than a preset emotion index value, determining market environment information as a first type environment; and under the condition that the total emotion index is smaller than the preset emotion index value, determining the market environment information as a second type environment.
Specifically, when the total emotion index is between-1 and 1, the emotion condition of the investor can be well indicated; when the total emotion index is greater than 0, indicating that the investor presents optimistic emotion to the current market and that the current environment is more positive; conversely, when the total emotion index is less than 0, it indicates that the investor has a more pessimistic emotion on the current market and the current environment is more negative.
For example, fig. 5 is a schematic diagram of an emotion distribution of an investor based on a time period according to an embodiment of the present application, as shown in fig. 5, an abscissa is a current target time period, for example, a year is a time period, an ordinate is a total emotion index, and scattered points in the graph are respectively calculated total emotion indexes based on emotion indexes of a plurality of positive emotions and emotion indexes of a plurality of negative emotions in the current time period. Most of the scattered points in the graph are distributed between-1 and 0, the current dominant emotion is represented as negative emotion, the negative emotion is distributed in a concentrated mode all the year, the annual investors are not good in the trend of the current financial market, and have negative attitudes, and if the problem is solved, corresponding measures are required to be issued to stimulate market economy and encourage the investors to conduct financial transactions.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowcharts, in some cases the steps illustrated or described may be performed in an order other than that illustrated herein.
The embodiment of the application also provides an analysis device for the business market environment, and it should be noted that the analysis device for the business market environment in the embodiment of the application can be used for executing the analysis method for the business market environment provided in the embodiment of the application. The following describes an analysis device for a business market environment provided in an embodiment of the present application.
Fig. 6 is a schematic diagram of an analysis device for a business market environment according to an embodiment of the present application, as shown in fig. 6, the device includes: an acquisition unit 60, a first input unit 61, a first determination unit 62.
An acquisition unit 60 for acquiring service information data within a target time period from a service information website of a target service, and screening text data from the service information data;
a first input unit 61, configured to input text data into a bi-directional coding model, and output a result of emotion inference of a transactor of a processing target service, where the bi-directional coding model is obtained by training a preset bi-directional coding model with M sets of sample data, and the M sets of sample data include text samples and emotion data of the text samples;
The first determining unit 62 is configured to determine market environment information of the target service according to the emotion inference result.
Optionally, in the analysis device for a business market environment provided in the embodiment of the present application, the obtaining unit 60 includes: the device comprises an acquisition module, a data cleaning module and a data processing module, wherein the acquisition module is used for acquiring a preset deletion program and a data cleaning rule, the data cleaning rule indicates the type of data to be deleted, the type of the data to be deleted comprises repeated text data, form data and label data, and the deletion program is used for deleting the data to be deleted; the screening module is used for screening the business information data based on the data cleaning rule by utilizing the deleting program to obtain N initial text data; and the processing module is used for processing the N initial text data to obtain text data.
Optionally, in the analysis device for a business market environment provided in the embodiment of the present application, the obtaining unit 60 includes: the first calculation module is used for calculating the influence degree parameter of each piece of initial text data, wherein the influence degree parameter refers to the influence degree value of the initial text data on the transaction of the transactor; the arrangement module is used for carrying out descending order arrangement on the N initial text data according to the influence degree parameters to obtain an initial text data sequence, and determining the initial text data with the preset quantity in the initial text data sequence as text data.
Optionally, in the analysis device for a business market environment provided in the embodiment of the present application, the training unit includes: the first composing module is used for composing a group of first sample data by M1 first text samples and emotion data of each first text sample to obtain M1 groups of first sample data; the first training module is used for training a preset bidirectional coding model through M1 groups of first sample data to obtain an initial bidirectional coding model; the second forming module is used for forming a group of second sample data by M2 second text samples and emotion data of each second text sample to obtain M2 groups of second sample data, wherein the sum of M1 and M2 is M, and the emotion value of the emotion data of the second sample data is larger than a preset emotion value; and the second training module is used for training the initial bidirectional coding model through M2 groups of second sample data to obtain a bidirectional coding model.
Optionally, in the analysis device for a business market environment provided in the embodiment of the present application, the device further includes: the screening unit is used for screening transaction index data from the business information data before market environment information of the target business is determined according to the emotion reasoning result, and calculating according to the transaction index data to obtain market environment data; the second input unit is used for inputting the text data and the market environment data into the time-varying parameter vector autoregressive model and outputting the emotion reasoning result of the transactor and the relativity of the market environment data; and the second determining unit is used for determining market environment information of the target service according to the emotion reasoning result under the condition that the relevance is larger than the relevance threshold value.
Optionally, in the analysis device for a business market environment provided in the embodiment of the present application, the first determining unit 62 includes: the second calculation module is used for carrying out weighted summation calculation on the forward emotion reasoning results corresponding to each text data in the target time period to obtain a first group of emotion indexes; the third calculation module is used for carrying out weighted summation calculation on negative emotion reasoning results corresponding to each text data in a target time period to obtain a second group of emotion indexes; and the fourth calculation module is used for calculating the total emotion index in the target time period by using the first group of emotion indexes and the second group of emotion indexes, and determining market environment information of the business according to the total emotion index.
Optionally, in the analysis device for a business market environment provided in the embodiment of the present application, the first determining unit 62 includes: the first determining module is used for determining that the market environment information is a first type environment under the condition that the total emotion index is larger than a preset emotion index value; and the second determining module is used for determining that the market environment information is of a second type environment under the condition that the total emotion index is smaller than a preset emotion index value.
The analysis device for the business market environment provided by the embodiment of the application is used for acquiring business information data in a target time period from a business information website of a target business through the acquisition unit 60, and screening text data from the business information data; a first input unit 61, configured to input text data into a bi-directional coding model, and output a result of emotion inference of a transactor of a processing target service, where the bi-directional coding model is obtained by training a preset bi-directional coding model with M sets of sample data, and the M sets of sample data include text samples and emotion data of the text samples; the first determining unit 62 is configured to determine market environment information of the target service according to the emotion inference result, solve the problem in the related art that it is difficult to accurately obtain the emotion of the investor and effectively identify the market environment by using the emotion of the investor, perform emotion classification of text data by using the trained bidirectional coding model, obtain the emotion inference result of the transactor, and determine the market environment information according to the emotion inference result, thereby achieving the effect of accurately determining the market environment information.
The analysis device of the business market environment includes a processor and a memory, the above-mentioned acquisition unit 60, the first input unit 61, the first determination unit 62, and the like are stored in the memory as program units, and the above-mentioned program units stored in the memory are executed by the processor to realize the corresponding functions.
The processor includes a kernel, and the kernel fetches the corresponding program unit from the memory. The kernel can be provided with one or more than one kernel, and the problem that the investor emotion is difficult to accurately acquire in the related technology and the market environment is effectively identified by utilizing the investor emotion is solved by adjusting the kernel parameters.
The memory may include volatile memory, random Access Memory (RAM), and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM), among other forms in computer readable media, the memory including at least one memory chip.
The embodiment of the invention provides a computer readable storage medium, on which a program is stored, which when executed by a processor, implements the method for analyzing a business market environment.
The embodiment of the invention provides a processor which is used for running a program, wherein the analysis method of the business market environment is executed when the program runs.
Fig. 7 is a schematic diagram of an electronic device according to an embodiment of the present application, and as shown in fig. 7, an embodiment of the present invention provides an electronic device, where the electronic device 70 includes a processor, a memory, and a program stored on the memory and executable on the processor, and the processor is configured to execute computer readable instructions, where the computer readable instructions execute a method for analyzing a business market environment when executed. The device herein may be a server, PC, PAD, cell phone, etc.
The present application also provides a computer program product adapted to perform an analysis method of performing a business market environment when executed on a data processing device.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and changes may be made to the present application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present application are intended to be included within the scope of the claims of the present application.
Claims (10)
1. A method of analyzing a business market environment, comprising:
acquiring business information data in a target time period from a business information website of a target business, and screening text data from the business information data;
inputting the text data into a bi-directional coding model, and outputting emotion reasoning results of a transactor for processing the target service, wherein the bi-directional coding model is obtained by training a preset bi-directional coding model by M groups of sample data, and the M groups of sample data comprise text samples and emotion data of the text samples;
and determining market environment information of the target service according to the emotion reasoning result.
2. The method of claim 1, wherein screening text data from the business information data comprises:
acquiring a preset deleting program and a data cleaning rule, wherein the data cleaning rule indicates the type of data to be deleted, the type of the data to be deleted comprises repeated text data, form data and tag data, and the deleting program is used for deleting the data to be deleted;
screening the business information data based on the data cleaning rule by utilizing the deleting program to obtain N initial text data;
And processing the N initial text data to obtain the text data.
3. The method of claim 2, wherein processing the N initial text data to obtain the text data comprises:
calculating an influence degree parameter of each piece of initial text data, wherein the influence degree parameter refers to an influence degree value of the initial text data on transaction of a transactor;
and performing descending order arrangement on the N initial text data according to the influence degree parameters to obtain an initial text data sequence, and determining the initial text data with the preset quantity in the initial text data sequence as the text data.
4. The method of claim 1, wherein the bi-directional coding model is trained by:
forming a group of first sample data by M1 first text samples and emotion data of each first text sample to obtain M1 groups of first sample data;
training the preset bidirectional coding model through the M1 group of first sample data to obtain an initial bidirectional coding model;
forming a group of second sample data by M2 second text samples and emotion data of each second text sample to obtain M2 groups of second sample data, wherein the sum of M1 and M2 is M, and the emotion value of the emotion data of the second sample data is larger than a preset emotion value;
And training the initial bidirectional coding model through the M2 groups of second sample data to obtain the bidirectional coding model.
5. The method of claim 1, wherein prior to determining market environment information for the target business based on the emotional inference results, the method further comprises:
screening transaction index data from the business information data, and calculating according to the transaction index data to obtain market environment data;
inputting the text data and the market environment data into a time-varying parameter vector autoregressive model, and outputting the emotion reasoning result of the transactor and the relativity of the market environment data;
and under the condition that the relevance is larger than a relevance threshold, determining market environment information of the target service according to the emotion reasoning result.
6. The method of claim 1, wherein the emotional inference results include a positive emotional inference result and a negative emotional inference result, and determining market environment information of the target service according to the emotional inference results comprises:
carrying out weighted summation calculation on forward emotion reasoning results corresponding to each text data in the target time period to obtain a first group of emotion indexes;
Carrying out weighted summation calculation on negative emotion reasoning results corresponding to each text data in the target time period to obtain a second group of emotion indexes;
and calculating a total emotion index in the target time period by using the first group of emotion indexes and the second group of emotion indexes, and determining market environment information of the business according to the total emotion index.
7. The method of claim 6, wherein determining market environment information for the target business based on the emotional inference results comprises:
determining that the market environment information is a first type environment under the condition that the total emotion index is larger than a preset emotion index value;
and under the condition that the total emotion index is smaller than the preset emotion index value, determining the market environment information as a second type environment.
8. An analysis device for a business market environment, comprising:
the acquisition unit is used for acquiring service information data in a target time period from a service information website of a target service and screening text data from the service information data;
the first input unit is used for inputting the text data into a bi-directional coding model and outputting emotion reasoning results of a transactor for processing the target service, wherein the bi-directional coding model is obtained by training a preset bi-directional coding model through M groups of sample data, and the M groups of sample data comprise text samples and emotion data of the text samples;
And the first determining unit is used for determining market environment information of the target service according to the emotion reasoning result.
9. A processor, characterized in that the processor is adapted to run a program, wherein the program when run performs the method of analyzing a business market environment according to any of claims 1 to 7.
10. An electronic device comprising one or more processors and a memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of analysis of a business market environment of any of claims 1-7.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116821489A (en) * | 2023-06-21 | 2023-09-29 | 易方达基金管理有限公司 | Stock screening method and system |
| CN116821489B (en) * | 2023-06-21 | 2024-05-10 | 易方达基金管理有限公司 | Stock screening method and system |
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