WO2019095569A1 - 基于微博财经事件的金融分析方法、应用服务器及计算机可读存储介质 - Google Patents

基于微博财经事件的金融分析方法、应用服务器及计算机可读存储介质 Download PDF

Info

Publication number
WO2019095569A1
WO2019095569A1 PCT/CN2018/076130 CN2018076130W WO2019095569A1 WO 2019095569 A1 WO2019095569 A1 WO 2019095569A1 CN 2018076130 W CN2018076130 W CN 2018076130W WO 2019095569 A1 WO2019095569 A1 WO 2019095569A1
Authority
WO
WIPO (PCT)
Prior art keywords
financial
microblog
event
hotspot
data stream
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/CN2018/076130
Other languages
English (en)
French (fr)
Inventor
王健宗
吴天博
黄章成
肖京
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Ping An Technology Shenzhen Co Ltd
Original Assignee
Ping An Technology Shenzhen Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Ping An Technology Shenzhen Co Ltd filed Critical Ping An Technology Shenzhen Co Ltd
Publication of WO2019095569A1 publication Critical patent/WO2019095569A1/zh
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/40—Business processes related to social networking or social networking services
    • G06Q10/44—Identification of trends within social networks, e.g. identification of trending topics
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/06—Asset management; Financial planning or analysis

Definitions

  • the present application relates to the field of financial data, and in particular, to a financial analysis method, an application server, and a computer readable storage medium based on a microblog financial event.
  • the financial sector As far as the financial sector is concerned, it mainly includes two aspects: on the one hand, asset prices are not affected by small factors as they were 30 years ago, but are affected by a combination of factors. On the other hand, when there are more and more relevant factors affecting assets, how to identify the correlation and causality behind the incident becomes more difficult.
  • the judgment of the general investors on financial investment is mainly based on personal investment experience and a perceptual judgment on various current financial events, but this method requires long-term investment experience and certain professional knowledge. For ordinary investors, there are still great difficulties and uncertainties in judging valuable information from a large number of financial events.
  • the present application proposes a financial analysis method, an application server and a computer readable storage medium based on the microblog financial event, which can conveniently, quickly and intuitively find current stock market data and finance in the hot and hot financial events.
  • Transaction data affects large financial and hot events, which in turn provides direct and effective reference for investors.
  • the present application provides an application server, where the application server includes a memory and a processor, where the memory stores a financial analysis program based on a Weibo financial event that can be run on the processor.
  • the financial analysis program based on the Weibo financial event is executed by the processor, the following steps are implemented:
  • the impact index is visualized.
  • the present application further provides a financial analysis method based on a microblog financial event, the method is applied to an application server, and the method includes:
  • the impact index is visualized.
  • the present application further provides a computer readable storage medium storing a financial analysis program based on a microblogging financial event, the financial analysis program based on a microblog financial event
  • the step of performing, by the at least one processor, the financial analysis method based on the Weibo financial event as described above may be performed by at least one processor.
  • the application server, the financial analysis method based on the microblog financial event, and the computer readable storage medium proposed by the present application first establish a financial microblog account pool, according to the financial microblog account pool. Obtaining a microblog data stream; secondly, obtaining a financial hotspot event according to the microblog data stream; then, constructing a model of the impact of the financial hotspot event on the stock market and the financial; and then, according to the obtained financial hotspot event and the impact The model analyzes the impact index of the financial hotspot event on the stock market and finance; finally, the impact index is visualized.
  • 1 is a schematic diagram of an optional hardware architecture of an application server of the present application
  • FIG. 2 is a program module diagram of a first embodiment of a financial analysis program based on a Weibo financial event according to the present application
  • FIG. 3 is a flowchart of a microblog hot topic discovery algorithm based on “acceleration” in a preferred embodiment of the present application
  • FIG. 4 is a flow chart of a first embodiment of a financial analysis method based on a microblogging financial event according to the present application.
  • FIG. 1 it is a schematic diagram of an optional hardware architecture of the application server 1.
  • the application server 1 may be a computing device such as a rack server, a blade server, a tower server, or a rack server.
  • the application server 1 may be a stand-alone server or a server cluster composed of multiple servers.
  • the application server 1 may include, but is not limited to, the memory 11, the processor 12, and the network interface 13 being communicably connected to each other through a system bus.
  • the application server 1 connects to the network through the network interface 13 to obtain information.
  • the network may be an intranet, an Internet, a Global System of Mobile communication (GSM), a Wideband Code Division Multiple Access (WCDMA), a 4G network, or a 5G network.
  • Wireless or wired networks such as networks, Bluetooth, Wi-Fi, and call networks.
  • Figure 1 only shows the application server 1 with components 11-13, but it should be understood that not all illustrated components may be implemented, and more or fewer components may be implemented instead.
  • the memory 11 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (eg, SD or DX memory, etc.), and a random access memory (RAM). , static random access memory (SRAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), programmable read only memory (PROM), magnetic memory, magnetic disk, optical disk, and the like.
  • the memory 11 may be an internal storage unit of the application server 1, such as a hard disk or memory of the application server 1.
  • the memory 11 may also be an external storage device of the application server 1, such as a plug-in hard disk equipped with the application server 1, a smart memory card (SMC), and a secure digital ( Secure Digital, SD) cards, flash cards, etc.
  • the memory 11 can also include both the internal storage unit of the application server 1 and its external storage device.
  • the memory 11 is generally used to store an operating system installed in the application server 1 and various types of application software, such as program code of the financial analysis program 200 based on the Weibo financial event. 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 application server 1, such as performing data interaction or communication related control and processing, and the like.
  • the processor 12 is configured to run program code or process data stored in the memory 11, such as running the financial analysis program 200 based on the microblog financial event.
  • 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 application server 1 and other electronic devices.
  • the application server 1 installs and runs a financial analysis program 200 based on the microblog financial event, and when the financial analysis program 200 based on the microblog financial event runs, the application server 1 establishes a financial class. a microblog account pool, obtaining a microblog data stream according to the financial microblog account pool; obtaining a financial hotspot event according to the microblog data stream; constructing an impact model of the financial hotspot event on the stock market and the financial; The financial hotspot event and the impact model analyze the impact index of the financial hotspot event on the stock market and the financial; and visualize the impact index.
  • the present application proposes a financial analysis program 200 based on Weibo financial events.
  • FIG. 2 it is a program module diagram of the first embodiment of the financial analysis program 200 based on the Weibo financial event.
  • the financial analysis program 200 based on the Weibo financial event includes a series of computer program instructions stored in the memory 11, and when the computer program instructions are executed by the processor 12, the implementation of the present application can be implemented.
  • the financial analysis program 200 based on the Weibo financial event may be divided into one or more modules based on the particular operations implemented by the various portions of the computer program instructions.
  • the financial analysis program 200 based on the microblog financial event may be divided into an establishing module 201, an obtaining module 202, a model building module 203, an analyzing module 204, and a display module 205. among them:
  • the establishing module 201 is configured to establish a financial microblog account pool. Specifically, the establishing module 201 is configured to extract a predetermined number of target accounts from a full amount of microblog accounts to establish the financial microblog account pool according to a preset extraction policy, where the full amount of microblog accounts refer to the application. All the microblog accounts monitored by the server 1, for example, the application server 1 monitors about 200,000 microblog accounts, and then extracts the full amount of microblog accounts according to the account extraction conditions and the extraction policy, wherein the so-called account extraction conditions and extraction
  • the policy may be a keyword policy, which refers to a preset financial keyword.
  • the establishing module 201 determines, according to the brief description of the microblog account, whether there is a preset financial keyword, and if so, the microblog account is included in the financial microblog account pool. In addition, the establishing module 201 can also search by using a preset microblog account list, for example, a person related to the financial industry, that is, directly inserting the microblog account of the relevant financial person into the financial microblog account pool.
  • a preset microblog account list for example, a person related to the financial industry, that is, directly inserting the microblog account of the relevant financial person into the financial microblog account pool.
  • the establishing module 202 is further configured to preset the update time of the financial microblog account pool. Then, the full amount of microblog accounts are extracted again according to the update time to update the financial class microblog account pool. For example, the establishing module 202 presets the financial microblog account pool to be updated once every 10 minutes.
  • the specific update time is set by the administrator according to the needs, and the application is not limited. In this embodiment, due to the real-time changing characteristics of the financial information, the accuracy and timeliness of the data transmitted by the established financial microblog account pool can be ensured by periodically updating the above-mentioned financial microblog account pool.
  • the obtaining module 202 is configured to obtain a microblog data stream according to the financial microblog account pool established by the establishing module 201, and obtain a financial hotspot event according to the microblog data stream.
  • the obtaining module 202 acquires a financial hotspot event from the microblog data stream based on a preset microblog hot topic discovery algorithm. For example, the "acceleration" of the microblog hot topic discovery algorithm.
  • FIG. 3 is a flowchart of a microblog hot topic discovery algorithm based on “acceleration”.
  • S"(t) is used as an indicator to measure the acceleration of the total microblog data stream, that is, the acceleration of the overall microblog stream;
  • X"(t) is used as an indicator to measure the acceleration of the first-order word frequency microblog data stream.
  • the first-order word frequency refers to the frequency of occurrence of a single word; Y"(t) is used as an index to measure the acceleration of the second-order word frequency microblog data stream, the second-order word frequency refers to the frequency at which two words appear simultaneously; the "gray box” is a sparse matrix non- The part of zero value, that is, the non-zero value part after word vectorization; the first-order word frequency of X"(t) is a vector, and the second-order word frequency of Y"(t) is a matrix.
  • Step (1) is to collect the microblog data and vectorize the representation to obtain S"(t).
  • Step (2) is to calculate the acceleration X"(t) of the first-order word frequency and the acceleration Y"(t) of the second-order word frequency.
  • Step (3) is to monitor and update the data stream in real time based on the results of the previous operation.
  • Step (4) is the notification and the summation is performed.
  • Step (5) is to output the result of the discovered hot event stream.
  • the topic of the word with the highest X"(t) and Y"(t) is a hot topic.
  • the model building module 203 is configured to construct an impact model of the financial hotspot event on the stock market and the financial.
  • model building module 203 constructs an impact model of the financial hotspot event on the stock market and the finance by:
  • the model building module 203 firstly acquires a large number of historical financial hotspot events, simultaneously acquires stock market data and financial transaction data at the same time as the historical financial hotspot event, and then compares the historical financial hotspot events with the stock market data and financial transactions.
  • the data is trained to obtain a functional relationship between the historical financial hotspot event and the stock market data and financial transaction data, and finally the impact model is constructed according to the function relationship.
  • the analyzing module 204 is configured to analyze an impact index of the financial hotspot event on the stock market and the financial according to the financial hotspot event acquired by the obtaining module 202 and the impact model constructed by the model building module 203.
  • the impact model has been determined through the above steps, and the financial hotspot event is imported into the impact model, thereby outputting an intuitive impact index.
  • the current financial hotspot event is that the central bank has lowered the deposit reserve ratio.
  • the hotspots are introduced into the above-mentioned impact model.
  • the deposit reserve ratio is lowered, the funds in the market are relatively more substantial, and investment activities are more accessible.
  • the impact model will output an estimated stock market upside index, such as how many points are expected to go up.
  • the display module 205 is configured to visualize the impact index obtained by the analysis module 204. Specifically, the impact index is visualized in the terminal device.
  • the terminal device may be a mobile phone, a smart phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a navigation device, and a vehicle.
  • a mobile device such as a device, and a fixed terminal such as a digital TV, a desktop computer, a notebook, a server, and the like.
  • the financial analysis program 200 based on the microblog financial event proposed by the present application firstly establishes a financial microblog account pool, and obtains a microblog data stream according to the financial microblog account pool; Obtaining a financial hotspot event according to the microblog data stream; then, constructing an impact model of the financial hotspot event on the stock market and the financial; and then analyzing the financial hotspot event according to the obtained financial hotspot event and the impact model The impact index on the stock market and finance; finally, the impact index is visualized.
  • the present application also proposes a financial analysis method based on Weibo financial events.
  • FIG. 4 it is a flowchart of the first embodiment of the financial analysis method based on the Weibo financial event of the present application.
  • the order of execution of the steps in the flowchart shown in FIG. 4 may be changed according to different requirements, and some steps may be omitted.
  • Step S401 establishing a financial microblog account pool.
  • the financial account type microblog account pool is established by extracting a preset number of target accounts from the full amount of microblog accounts according to a preset extraction policy, where the full amount of microblog accounts refers to all monitored by the application server 1
  • the microblog account for example, the application server 1 monitors about 200,000 microblog accounts, and then extracts the full amount of microblog accounts according to the account extraction conditions and the extraction policy, wherein the so-called account extraction conditions and extraction policies may be keywords.
  • Policy the keyword policy refers to a preset financial keyword.
  • the application server 1 determines whether there is a preset financial keyword according to the brief description of the microblog account, and if so, the microblog account is included in the financial microblog account pool.
  • the application server 1 can also search through a preset microblog account list, for example, a person related to the financial industry, that is, directly insert the microblog account of the relevant financial person into the financial microblog account pool.
  • the application server 1 is further configured to preset the update time of the financial microblog account pool. Then, the full amount of microblog accounts are extracted again according to the update time to update the financial class microblog account pool. For example, the application server 1 presets the financial class microblog account pool to be updated once every 10 minutes.
  • the specific update time is set by the administrator according to the needs, and the application is not limited.
  • the accuracy and timeliness of the data transmitted by the established financial microblog account pool can be ensured by periodically updating the above-mentioned financial microblog account pool.
  • Step S402 Acquire a microblog data stream according to the financial microblog account pool, and obtain a financial hotspot event according to the microblog data stream.
  • the application server 1 acquires a financial hotspot event from the microblog data stream based on a preset microblog hot topic discovery algorithm. For example, the "acceleration" of the microblog hot topic discovery algorithm.
  • FIG. 3 is a flowchart of a microblog hot topic discovery algorithm based on “acceleration”.
  • S"(t) is used as an indicator to measure the acceleration of the total microblog data stream, that is, the acceleration of the overall microblog stream;
  • X"(t) is used as an indicator to measure the acceleration of the first-order word frequency microblog data stream.
  • the first-order word frequency refers to the frequency of occurrence of a single word; Y"(t) is used as an index to measure the acceleration of the second-order word frequency microblog data stream, the second-order word frequency refers to the frequency at which two words appear simultaneously; the "gray box” is a sparse matrix non- The part of zero value, that is, the non-zero value part after word vectorization; the first-order word frequency of X"(t) is a vector, and the second-order word frequency of Y"(t) is a matrix.
  • Step (1) is to collect the microblog data and vectorize the representation to obtain S"(t).
  • Step (2) is to calculate the acceleration X"(t) of the first-order word frequency and the acceleration Y"(t) of the second-order word frequency.
  • Step (3) is to monitor and update the data stream in real time based on the results of the previous operation.
  • Step (4) is the notification and the summation is performed.
  • Step (5) is to output the result of the discovered hot event stream.
  • the topic of the word with the highest X"(t) and Y"(t) is a hot topic.
  • Step S403 constructing an impact model of the financial hotspot event on the stock market and the financial.
  • the application server 1 constructs an impact model of the financial hotspot event on the stock market and the finance by:
  • the application server 1 firstly acquires a large number of historical financial hotspot events, and simultaneously acquires stock market data and financial transaction data at the same time as the historical financial hotspot event, and then the historical financial hotspot event and the stock market data and financial transaction data. Performing training to obtain a functional relationship between the historical financial hotspot event and the stock market data and financial transaction data, and finally constructing the impact model according to the function relationship.
  • Step S404 Analyze an impact index of the financial hotspot event on the stock market and the finance according to the obtained financial hot spot event and the impact model.
  • the impact model has been determined through the above steps, and the financial hotspot event is imported into the impact model, thereby outputting an intuitive impact index.
  • the current financial hotspot event is that the central bank has lowered the deposit reserve ratio.
  • the hotspots are introduced into the above-mentioned impact model.
  • the deposit reserve ratio is lowered, the funds in the market are relatively more substantial, and investment activities are more accessible.
  • the impact model will output an estimated stock market upside index, such as how many points are expected to go up.
  • Step S405 visualizing the impact index.
  • the impact index is visualized in the terminal device.
  • the terminal device may be a mobile phone, a smart phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a navigation device, and a vehicle.
  • a mobile device such as a device, and a fixed terminal such as a digital TV, a desktop computer, a notebook, a server, and the like.
  • the financial analysis method based on the microblog financial event proposed by the present application firstly establishes a financial microblog account pool, and obtains a microblog data stream according to the financial microblog account pool; secondly, according to The microblog data stream obtains a financial hotspot event; then, constructs a model of the impact of the financial hotspot event on the stock market and the financial; and then analyzes the financial hotspot event against the stock market according to the obtained financial hotspot event and the impact model And the financial impact index; finally, the impact index is visualized.
  • 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 the various embodiments of the present application.

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Marketing (AREA)
  • Strategic Management (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Finance (AREA)
  • Theoretical Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • Operations Research (AREA)
  • Economics (AREA)
  • Accounting & Taxation (AREA)
  • Development Economics (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • Technology Law (AREA)
  • Game Theory and Decision Science (AREA)
  • Quality & Reliability (AREA)
  • Tourism & Hospitality (AREA)
  • Financial Or Insurance-Related Operations Such As Payment And Settlement (AREA)

Abstract

本申请公开了一种基于微博财经事件的金融分析方法，所述方法包括：通过爬虫程序爬取目标金融公司的关联数据；对所述关联数据进行预处理；对预处理后的关联数据进行文本分词，获取文本集合；分析所述文本集合获取主题集；计算所述文本集合的关键词集；选出所述关键词集中与所述主题集相匹配的关键词；选择公众对所述目标金融公司的期望词，并通过预设模型计算所述期望词与所述关键词的共现程度；及基于所述共现程度输出对所述目标金融公司的评价结论。本申请还提供一种应用服务器及计算机可读存储介质。本申请提供的金融数据分析方法、应用服务器及计算机可读存储介质，可以快速获取目标金融公司实施政策在公众眼中的态度数据，促进相关业务发展。

Description

基于微博财经事件的金融分析方法、应用服务器及计算机可读存储介质
本申请要求于2017年11月17日提交中国专利局、申请号为201711141740.7、发明名称为“基于微博财经事件的金融分析方法、应用服务器及计算机可读存储介质”的中国专利申请的优先权，其全部内容通过引用结合在申请中。
技术领域
本申请涉及金融数据领域，尤其涉及一种基于微博财经事件的金融分析方法、应用服务器及计算机可读存储介质。
背景技术
就金融领域来讲，主要包括两个方面的问题：一方面，资产价格已不像30年前一样受小类因素的影响，而是受到多种因素的综合影响。另一方面，当影响资产的相关因素越来越多的时候，如何识别事件背后的相关性和因果性就变得更加困难。而目前广大普通投资者对金融投资的判断上面主要依据的是个人的投资经验和对当前各种财经事件的一个感性的判断，但这种方式需要长时间的投资经验以及一定的专业知识，而对于普通投资者而言，从大量的财经事件中判断出有价值的信息还存在很大的困难和不确定性。
发明内容
有鉴于此，本申请提出一种基于微博财经事件的金融分析方法、应用服务器及计算机可读存储介质，可以在浩如烟海的财经热点事件中，方便、快捷、直观的发现对当前股市数据和金融交易数据影响较大的财经热点事件，进而为广大投资者提供直接有效的参考。
首先，为实现上述目的，本申请提出一种应用服务器，所述应用服务器 包括存储器、处理器，所述存储器上存储有可在所述处理器上运行的基于微博财经事件的金融分析程序，所述基于微博财经事件的金融分析程序被所述处理器执行时，实现如下步骤：
建立财经类微博账号池，根据所述财经类微博账号池获取微博数据流；
根据所述微博数据流获取财经热点事件；
构建所述财经热点事件对股市和金融的影响模型；
根据获取的所述财经热点事件和所述影响模型分析所述财经热点事件对股市和金融的影响指数；及
将所述影响指数进行可视化呈现。
此外，为实现上述目的，本申请还提供一种基于微博财经事件的金融分析方法，该方法应用于应用服务器，所述方法包括：
建立财经类微博账号池，根据所述财经类微博账号池获取微博数据流；
根据所述微博数据流获取财经热点事件；
构建所述财经热点事件对股市和金融的影响模型；
根据获取的所述财经热点事件和所述影响模型分析所述财经热点事件对股市和金融的影响指数；及
将所述影响指数进行可视化呈现。
进一步地，为实现上述目的，本申请还提供一种计算机可读存储介质，所述计算机可读存储介质存储有基于微博财经事件的金融分析程序，所述基于微博财经事件的金融分析程序可被至少一个处理器执行，以使所述至少一个处理器执行如上述的基于微博财经事件的金融分析方法的步骤。
相较于现有技术，本申请所提出的应用服务器、基于微博财经事件的金融分析方法及计算机可读存储介质，首先，建立财经类微博账号池，根据所述财经类微博账号池获取微博数据流；其次，根据所述微博数据流获取财经热点事件；然后，构建所述财经热点事件对股市和金融的影响模型；接着，根据获取的所述财经热点事件和所述影响模型分析所述财经热点事件对股市 和金融的影响指数；最后，将所述影响指数进行可视化呈现。这样，可以避免现有技术中普通投资者从大量的财经事件中判断出有价值的信息还存在很大的困难和不确定性的弊端，让投资者可以在浩如烟海的财经热点事件中，方便、快捷、直观的发现对当前股市数据和金融交易数据影响较大的财经热点事件，进而为广大投资者提供直接有效的参考。
附图说明
图1是本申请应用服务器一可选的硬件架构的示意图；
图2是本申请基于微博财经事件的金融分析程序第一实施例的程序模块图；
图3为本申请较优实施例中基于“加速度”的微博热点话题发现算法的流程图；
图4为本申请基于微博财经事件的金融分析方法第一实施例的流程图。
附图标记：
Figure PCTCN2018076130-appb-000001
Figure PCTCN2018076130-appb-000002
本申请目的的实现、功能特点及优点将结合实施例，参照附图做进一步说明。
具体实施方式
为了使本申请的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本申请进行进一步详细说明。应当理解，此处所描述的具体实施例仅用以解释本申请，并不用于限定本申请。基于本申请中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
需要说明的是，在本申请中涉及“第一”、“第二”等的描述仅用于描述目的，而不能理解为指示或暗示其相对重要性或者隐含指明所指示的技术特征的数量。由此，限定有“第一”、“第二”的特征可以明示或者隐含地包括至少一个该特征。另外，各个实施例之间的技术方案可以相互结合，但是必须是以本领域普通技术人员能够实现为基础，当技术方案的结合出现相互矛盾或无法实现时应当认为这种技术方案的结合不存在，也不在本申请要求的保护范围之内。
参阅图1所示，是应用服务器1一可选的硬件架构的示意图。
所述应用服务器1可以是机架式服务器、刀片式服务器、塔式服务器或机柜式服务器等计算设备，该应用服务器1可以是独立的服务器，也可以是多个服务器所组成的服务器集群。
本实施例中，所述应用服务器1可包括，但不仅限于，可通过系统总线相互通信连接存储器11、处理器12、网络接口13。
所述应用服务器1通过网络接口13连接网络，获取资讯。所述网络可以是企业内部网(Intranet)、互联网(Internet)、全球移动通讯系统(Global System  of Mobile communication，GSM)、宽带码分多址(Wideband Code Division Multiple Access，WCDMA)、4G网络、5G网络、蓝牙(Bluetooth)、Wi-Fi、通话网络等无线或有线网络。
需要指出的是，图1仅示出了具有组件11-13的应用服务器1，但是应理解的是，并不要求实施所有示出的组件，可以替代的实施更多或者更少的组件。
其中，所述存储器11至少包括一种类型的可读存储介质，所述可读存储介质包括闪存、硬盘、多媒体卡、卡型存储器(例如，SD或DX存储器等)、随机访问存储器(RAM)、静态随机访问存储器(SRAM)、只读存储器(ROM)、电可擦除可编程只读存储器(EEPROM)、可编程只读存储器(PROM)、磁性存储器、磁盘、光盘等。在一些实施例中，所述存储器11可以是所述应用服务器1的内部存储单元，例如该应用服务器1的硬盘或内存。在另一些实施例中，所述存储器11也可以是所述应用服务器1的外部存储设备，例如该应用服务器1配备的插接式硬盘，智能存储卡(Smart Media Card,SMC)，安全数字(Secure Digital,SD)卡，闪存卡(Flash Card)等。当然，所述存储器11还可以既包括所述应用服务器1的内部存储单元也包括其外部存储设备。本实施例中，所述存储器11通常用于存储安装于所述应用服务器1的操作系统和各类应用软件，例如基于微博财经事件的金融分析程序200的程序代码等。此外，所述存储器11还可以用于暂时地存储已经输出或者将要输出的各类数据。
所述处理器12在一些实施例中可以是中央处理器(Central Processing Unit，CPU)、控制器、微控制器、微处理器、或其他数据处理芯片。该处理器12通常用于控制所述应用服务器1的总体操作，例如执行数据交互或者通信相关的控制和处理等。本实施例中，所述处理器12用于运行所述存储器11中存储的程序代码或者处理数据，例如运行所述的基于微博财经事件的金融分析程序200等。
所述网络接口13可包括无线网络接口或有线网络接口，该网络接口13通常用于在所述应用服务器1与其他电子设备之间建立通信连接。
本实施例中，所述应用服务器1内安装并运行有基于微博财经事件的金融分析程序200，当所述基于微博财经事件的金融分析程序200运行时，所述应用服务器1建立财经类微博账号池，根据所述财经类微博账号池获取微博数据流；根据所述微博数据流获取财经热点事件；构建所述财经热点事件对股市和金融的影响模型；根据获取的所述财经热点事件和所述影响模型分析所述财经热点事件对股市和金融的影响指数；并将所述影响指数进行可视化呈现。这样，可以避免现有技术中普通投资者从大量的财经事件中判断出有价值的信息还存在很大的困难和不确定性的弊端，让投资者可以在浩如烟海的财经热点事件中，方便、快捷、直观的发现对当前股市数据和金融交易数据影响较大的财经热点事件，进而为广大投资者提供直接有效的参考。
至此，己经详细介绍了本申请各个实施例的相关设备的硬件结构和功能。下面，将基于上述应用环境和相关设备，提出本申请的各个实施例。
首先，本申请提出一种基于微博财经事件的金融分析程序200。
参阅图2所示，是本申请基于微博财经事件的金融分析程序200第一实施例的程序模块图。
本实施例中，所述的基于微博财经事件的金融分析程序200包括一系列的存储于存储器11上的计算机程序指令，当该计算机程序指令被处理器12执行时，可以实现本申请各实施例的基于微博财经事件的金融分析操作。在一些实施例中，基于该计算机程序指令各部分所实现的特定的操作，所述基于微博财经事件的金融分析程序200可以被划分为一个或多个模块。例如，在图2中，所述的基于微博财经事件的金融分析程序200可以被分割成建立模块201、获取模块202、模型构建模块203、分析模块204以及显示模块205。其中：
所述建立模块201，用于建立财经类微博账号池。具体地，所述建立模块201根据预设的抽取策略从全量微博账号中抽取预设数量的目标账号建立所述财经类微博账号池，其中，所述全量微博账号是指所述应用服务器1监控 的所有微博账号，例如，所述应用服务器1监控20万左右的微博账号，然后根据账号抽取条件和抽取策略对上述全量微博账号进行抽取，其中所谓的账号抽取条件和抽取策略可以是关键字策略，所述关键字策略是指预设金融关键字。所述建立模块201根据微博账号的简要说明中判断出是否存在预设的金融关键字，如果存在的话，则将该微博账号纳入财经类微博账号池。另外，所述建立模块201也可以通过预设微博账号名单进行搜寻，比如与财经行业相关的人士，即直接把相关财经人士的微博账号纳入财经类微博账号池。
本实施例中，进一步地，为了确保所建立的财经类微博账号池所传递的数据的准确性和及时性，所述建立模块202还用于预设财经类微博账号池的更新时间，然后根据所述更新时间再次抽取所述全量微博账号以更新所述财经类微博账号池。例如，所述建立模块202预设财经类微博账号池10分钟更新一次，当然具体地更新时间由管理人员根据需要自行设定，本申请并不作限定。本实施例中，由于财经类信息的实时变化的特性，通过定期的更新上述财经类微博账号池，可以确保所建立的财经类微博账号池所传递的数据的准确性和及时性。
所述获取模块202，用于根据所述建立模块201建立的财经类微博账号池获取微博数据流，并根据所述微博数据流获取财经热点事件。
具体地，所述获取模块202基于预设的微博热点话题发现算法从所述微博数据流中获取财经热点事件。例如，“加速度”的微博热点话题发现算法。
其中，图3为基于“加速度”的微博热点话题发现算法的流程图。参考图3，S”(t)作为衡量总量微博数据流加速度的一个指标，即整体微博流出现的加速度；X”(t)作为衡量一阶词频微博数据流加速度的一个指标，一阶词频指单个词出现的频率；Y”(t)作为衡量二阶词频微博数据流加速度的一个指标，二阶词频指同时两个词出现的频率；“灰色方框”是稀疏矩阵非零值的部分，即词向量化后的非零值部分；X”(t)一阶词频是向量，Y”(t)二阶词频是矩阵。
基于“加速度”的微博热点话题发现算法的具体运行流程如下：
第(1)步是采集微博数据后，向量化表示，得到S”(t)。
第(2)步是对一阶词频的加速度X”(t)和二阶词频的加速度Y”(t)进行计算。
第(3)步是根据上一步运算结果实时监视，并更新数据流。
第(4)步是通知，并进行加和汇总。
第(5)步是输出发现的热点事件流结果。即将X”(t)和Y”(t)最高的词所处话题作为热点话题。
所述模型构建模块203，用于构建所述财经热点事件对股市和金融的影响模型。
本实施例中，所述模型构建模块203通过以下方式构建所述财经热点事件对股市和金融的影响模型：
所述模型构建模块203首先，获取大量历史财经热点事件，同时获取与所述历史财经热点事件同时期的股市数据和金融交易数据，然后将所述历史财经热点事件与所述股市数据和金融交易数据进行训练获取所述历史财经热点事件与所述股市数据和金融交易数据的函数关系，最后根据所述函数关系构建所述影响模型。
本实施例中，通过大量的数据训练，可以获取两个变量之间的一种函数关系，进而在确定好上述影响模型后，并获取了新的财经热点事件后，将新的财经热点事件导入到上述影响模型后，便可以得出该财经热点事件下的股市和金融交易数据变化，进而可以给用户一个直观、科学的结论呈现。
所述分析模块204，用于根据所述获取模块202获取的所述财经热点事件和所述模型构建模块203构建的影响模型分析所述财经热点事件对股市和金融的影响指数。
本实施例中，通过上述步骤已经确定了所述影响模型，并将所述财经热点事件导入到所述影响模型中，进而输出一个直观的影响指数。比如当前的财经热点事件是央行降低了存款准备金率，那么将此种热点事情导入到上述 影响模型中，由于降低了存款准备金率，市场上的资金则相对更加充实，投资活动更加获取，则此时影响模型则会输出一个预估的股市上行指数，比如预计上行多少点。
所述显示模块205，用于将所述分析模块204分析所得的影响指数进行可视化呈现。具体地，所述影响指数可视化呈现在终端设备。本实施例中，所述终端设备可以是移动电话、智能电话、笔记本电脑、数字广播接收器、PDA(个人数字助理)、PAD(平板电脑)、PMP(便携式多媒体播放器)、导航装置、车载装置等等的可移动设备，以及诸如数字TV、台式计算机、笔记本、服务器等等的固定终端。
通过上述程序模块201-205，本申请所提出的基于微博财经事件的金融分析程序200，首先，建立财经类微博账号池，根据所述财经类微博账号池获取微博数据流；其次，根据所述微博数据流获取财经热点事件；然后，构建所述财经热点事件对股市和金融的影响模型；接着，根据获取的所述财经热点事件和所述影响模型分析所述财经热点事件对股市和金融的影响指数；最后，将所述影响指数进行可视化呈现。这样，可以避免现有技术中普通投资者从大量的财经事件中判断出有价值的信息还存在很大的困难和不确定性的弊端，让投资者可以在浩如烟海的财经热点事件中，方便、快捷、直观的发现对当前股市数据和金融交易数据影响较大的财经热点事件，进而为广大投资者提供直接有效的参考。
此外，本申请还提出一种基于微博财经事件的金融分析方法。
参阅图4所示，是本申请基于微博财经事件的金融分析方法第一实施例的流程图。在本实施例中，根据不同的需求，图4所示的流程图中的步骤的执行顺序可以改变，某些步骤可以省略。
步骤S401，建立财经类微博账号池。具体地，根据预设的抽取策略从全 量微博账号中抽取预设数量的目标账号建立所述财经类微博账号池，其中，所述全量微博账号是指所述应用服务器1监控的所有微博账号，例如，所述应用服务器1监控20万左右的微博账号，然后根据账号抽取条件和抽取策略对上述全量微博账号进行抽取，其中所谓的账号抽取条件和抽取策略可以是关键字策略，所述关键字策略是指预设金融关键字。所述应用服务器1根据微博账号的简要说明中判断出是否存在预设的金融关键字，如果存在的话，则将该微博账号纳入财经类微博账号池。另外，所述应用服务器1也可以通过预设微博账号名单进行搜寻，比如与财经行业相关的人士，即直接把相关财经人士的微博账号纳入财经类微博账号池。
本实施例中，进一步地，为了确保所建立的财经类微博账号池所传递的数据的准确性和及时性，所述应用服务器1还用于预设财经类微博账号池的更新时间，然后根据所述更新时间再次抽取所述全量微博账号以更新所述财经类微博账号池。例如，所述应用服务器1预设财经类微博账号池10分钟更新一次，当然具体地更新时间由管理人员根据需要自行设定，本申请并不作限定。本实施例中，由于财经类信息的实时变化的特性，通过定期的更新上述财经类微博账号池，可以确保所建立的财经类微博账号池所传递的数据的准确性和及时性。
步骤S402，根据所述财经类微博账号池获取微博数据流，并根据所述微博数据流获取财经热点事件。具体地，所述应用服务器1基于预设的微博热点话题发现算法从所述微博数据流中获取财经热点事件。例如，“加速度”的微博热点话题发现算法。
其中，图3为基于“加速度”的微博热点话题发现算法的流程图。参考图3，S”(t)作为衡量总量微博数据流加速度的一个指标，即整体微博流出现的加速度；X”(t)作为衡量一阶词频微博数据流加速度的一个指标，一阶词频指单个词出现的频率；Y”(t)作为衡量二阶词频微博数据流加速度的一个指标，二阶词频指同时两个词出现的频率；“灰色方框”是稀疏矩阵非零值的部分， 即词向量化后的非零值部分；X”(t)一阶词频是向量，Y”(t)二阶词频是矩阵。
基于“加速度”的微博热点话题发现算法的具体运行流程如下：
第(1)步是采集微博数据后，向量化表示，得到S”(t)。
第(2)步是对一阶词频的加速度X”(t)和二阶词频的加速度Y”(t)进行计算。
第(3)步是根据上一步运算结果实时监视，并更新数据流。
第(4)步是通知，并进行加和汇总。
第(5)步是输出发现的热点事件流结果。即将X”(t)和Y”(t)最高的词所处话题作为热点话题。
步骤S403，构建所述财经热点事件对股市和金融的影响模型。本实施例中，所述应用服务器1通过以下方式构建所述财经热点事件对股市和金融的影响模型：
所述应用服务器1首先，获取大量历史财经热点事件，同时获取与所述历史财经热点事件同时期的股市数据和金融交易数据，然后将所述历史财经热点事件与所述股市数据和金融交易数据进行训练获取所述历史财经热点事件与所述股市数据和金融交易数据的函数关系，最后根据所述函数关系构建所述影响模型。
本实施例中，通过大量的数据训练，可以获取两个变量之间的一种函数关系，进而在确定好上述影响模型后，并获取了新的财经热点事件后，将新的财经热点事件导入到上述影响模型后，便可以得出该财经热点事件下的股市和金融交易数据变化，进而可以给用户一个直观、科学的结论呈现。
步骤S404，根据获取的所述财经热点事件和所述影响模型分析所述财经热点事件对股市和金融的影响指数。
本实施例中，通过上述步骤已经确定了所述影响模型，并将所述财经热点事件导入到所述影响模型中，进而输出一个直观的影响指数。比如当前的财经热点事件是央行降低了存款准备金率，那么将此种热点事情导入到上述 影响模型中，由于降低了存款准备金率，市场上的资金则相对更加充实，投资活动更加获取，则此时影响模型则会输出一个预估的股市上行指数，比如预计上行多少点。
步骤S405，将所述影响指数进行可视化呈现。具体地，所述影响指数可视化呈现在终端设备。本实施例中，所述终端设备可以是移动电话、智能电话、笔记本电脑、数字广播接收器、PDA(个人数字助理)、PAD(平板电脑)、PMP(便携式多媒体播放器)、导航装置、车载装置等等的可移动设备，以及诸如数字TV、台式计算机、笔记本、服务器等等的固定终端。
通过上述步骤S401-405，本申请所提出的基于微博财经事件的金融分析方法，首先，建立财经类微博账号池，根据所述财经类微博账号池获取微博数据流；其次，根据所述微博数据流获取财经热点事件；然后，构建所述财经热点事件对股市和金融的影响模型；接着，根据获取的所述财经热点事件和所述影响模型分析所述财经热点事件对股市和金融的影响指数；最后，将所述影响指数进行可视化呈现。这样，可以避免现有技术中普通投资者从大量的财经事件中判断出有价值的信息还存在很大的困难和不确定性的弊端，让投资者可以在浩如烟海的财经热点事件中，方便、快捷、直观的发现对当前股市数据和金融交易数据影响较大的财经热点事件，进而为广大投资者提供直接有效的参考。
上述本申请实施例序号仅仅为了描述，不代表实施例的优劣。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到上述实施例方法可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件，但很多情况下前者是更佳的实施方式。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质(如ROM/RAM、磁碟、光盘) 中，包括若干指令用以使得一台终端设备(可以是手机，计算机，服务器，空调器，或者网络设备等)执行本申请各个实施例所述的方法。
以上仅为本申请的优选实施例，并非因此限制本申请的专利范围，凡是利用本申请说明书及附图内容所作的等效结构或等效流程变换，或直接或间接运用在其他相关的技术领域，均同理包括在本申请的专利保护范围内。

Claims (20)

  1. 一种基于微博财经事件的金融分析方法，应用于应用服务器，其特征在于，所述方法包括步骤：
    建立财经类微博账号池，根据所述财经类微博账号池获取微博数据流；
    根据所述微博数据流获取财经热点事件；
    构建所述财经热点事件对股市和金融的影响模型；
    根据获取的所述财经热点事件和所述影响模型分析所述财经热点事件对股市和金融的影响指数；及
    将所述影响指数进行可视化呈现。
  2. 如权利要求1所述的基于微博财经事件的金融分析方法，其特征在于，所述建立财经类微博账号池的步骤，包括步骤：
    根据预设的抽取策略从全量微博账号中抽取预设数量的目标账号建立所述财经类微博账号池，其中，所述全量微博账号是指所述应用服务器监控的所有微博账号。
  3. 如权利要求1所述的基于微博财经事件的金融分析方法，其特征在于，所述根据所述微博数据流获取财经热点事件的步骤，包括：
    基于预设的微博热点话题发现算法从所述微博数据流中获取财经热点事件。
  4. 如权利要求2所述的基于微博财经事件的金融分析方法，其特征在于，所述根据所述微博数据流获取财经热点事件的步骤，包括：
    基于预设的微博热点话题发现算法从所述微博数据流中获取财经热点事件。
  5. 如权利要求3所述的基于微博财经事件的金融分析方法，其特征在于，所述构建所述财经热点事件对股市和金融的影响模型的步骤，包括：
    获取大量历史财经热点事件；
    获取与所述历史财经热点事件同时期的股市数据和金融交易数据；
    将所述历史财经热点事件与所述股市数据和金融交易数据进行训练获取所述历史财经热点事件与所述股市数据和金融交易数据的函数关系；及
    根据所述函数关系构建所述影响模型。
  6. 如权利要求1所述的基于微博财经事件的金融分析方法，其特征在于，所述方法还包括步骤：
    预设财经类微博账号池的更新时间；及
    根据所述更新时间再次抽取所述全量微博账号以更新所述财经类微博账号池。
  7. 如权利要求2所述的基于微博财经事件的金融分析方法，其特征在于，所述方法还包括步骤：
    预设财经类微博账号池的更新时间；及
    根据所述更新时间再次抽取所述全量微博账号以更新所述财经类微博账号池。
  8. 如权利要求3所述的基于微博财经事件的金融分析方法，其特征在于，所述方法还包括步骤：
    预设财经类微博账号池的更新时间；及
    根据所述更新时间再次抽取所述全量微博账号以更新所述财经类微博账号池。
  9. 如权利要求5所述的基于微博财经事件的金融分析方法，其特征在于，所述方法还包括步骤：
    预设财经类微博账号池的更新时间；及
    根据所述更新时间再次抽取所述全量微博账号以更新所述财经类微博账号池。
  10. 一种应用服务器，其特征在于，所述应用服务器包括存储器、处理器，所述存储器上存储有可在所述处理器上运行的基于微博财经事件的金融分析程序，所述基于微博财经事件的金融分析程序被所述处理器执行时实现如下 步骤：
    建立财经类微博账号池，根据所述财经类微博账号池获取微博数据流；
    根据所述微博数据流获取财经热点事件；
    构建所述财经热点事件对股市和金融的影响模型；
    根据获取的所述财经热点事件和所述影响模型分析所述财经热点事件对股市和金融的影响指数；及
    将所述影响指数进行可视化呈现。
  11. 如权利要求10所述的应用服务器，其特征在于，所述建立财经类微博账号池的步骤，包括：
    根据预设的抽取策略从全量微博账号中抽取预设数量的目标账号建立所述财经类微博账号池，其中，所述全量微博账号是指所述应用服务器监控的所有微博账号。
  12. 如权利要求10所述的应用服务器，其特征在于，所述根据所述微博数据流获取财经热点事件的步骤，包括：
    基于预设的微博热点话题发现算法从所述微博数据流中获取财经热点事件。
  13. 如权利要求11所述的应用服务器，其特征在于，所述根据所述微博数据流获取财经热点事件的步骤，包括：
    基于预设的微博热点话题发现算法从所述微博数据流中获取财经热点事件。
  14. 如权利要求12所述的应用服务器，其特征在于，所述构建所述财经热点事件对股市和金融的影响模型的步骤，包括：
    获取大量历史财经热点事件；
    获取与所述历史财经热点事件同时期的股市数据和金融交易数据；
    将所述历史财经热点事件与所述股市数据和金融交易数据进行训练获取所述历史财经热点事件与所述股市数据和金融交易数据的函数关系；及
    根据所述函数关系构建所述影响模型。
  15. 如权利要求11所述的应用服务器，其特征在于，所述基于微博财经事件的金融分析程序被所述处理器执行时实现如下步骤：
    预设财经类微博账号池的更新时间；及
    根据所述更新时间再次抽取所述全量微博账号以更新所述财经类微博账号池。
  16. 一种计算机可读存储介质，所述计算机可读存储介质存储有基于微博财经事件的金融分析程序，所述基于微博财经事件的金融分析程序可被至少一个处理器执行，以使所述至少一个处理器执行时实现如下步骤：
    建立财经类微博账号池，根据所述财经类微博账号池获取微博数据流；
    根据所述微博数据流获取财经热点事件；
    构建所述财经热点事件对股市和金融的影响模型；
    根据获取的所述财经热点事件和所述影响模型分析所述财经热点事件对股市和金融的影响指数；及
    将所述影响指数进行可视化呈现。
  17. 如权利要求16所述的计算机可读存储介质，其特征在于，所述建立财经类微博账号池的步骤，包括：
    根据预设的抽取策略从全量微博账号中抽取预设数量的目标账号建立所述财经类微博账号池，其中，所述全量微博账号是指所述应用服务器监控的所有微博账号。
  18. 如权利要求16所述的计算机可读存储介质，其特征在于，所述根据所述微博数据流获取财经热点事件的步骤，包括：
    基于预设的微博热点话题发现算法从所述微博数据流中获取财经热点事件。
  19. 如权利要求18所述的计算机可读存储介质，其特征在于，所述根据所述微博数据流获取财经热点事件的步骤，包括：
    基于预设的微博热点话题发现算法从所述微博数据流中获取财经热点事件。
  20. 如权利要求16所述的计算机可读存储介质，其特征在于，所述基于微博财经事件的金融分析程序被所述处理器执行时实现如下步骤：
    预设财经类微博账号池的更新时间；及
    根据所述更新时间再次抽取所述全量微博账号以更新所述财经类微博账号池。
PCT/CN2018/076130 2017-11-17 2018-02-10 基于微博财经事件的金融分析方法、应用服务器及计算机可读存储介质 Ceased WO2019095569A1 (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201711141740.7A CN107945034A (zh) 2017-11-17 2017-11-17 基于微博财经事件的金融分析方法、应用服务器及计算机可读存储介质
CN201711141740.7 2017-11-17

Publications (1)

Publication Number Publication Date
WO2019095569A1 true WO2019095569A1 (zh) 2019-05-23

Family

ID=61931578

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2018/076130 Ceased WO2019095569A1 (zh) 2017-11-17 2018-02-10 基于微博财经事件的金融分析方法、应用服务器及计算机可读存储介质

Country Status (2)

Country Link
CN (1) CN107945034A (zh)
WO (1) WO2019095569A1 (zh)

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110209930A (zh) * 2019-04-30 2019-09-06 平安科技（深圳）有限公司 基于知识图谱的投资对象分析方法及装置
CN110750622A (zh) * 2019-09-17 2020-02-04 南京理工大学 基于大数据的金融事件发现方法
CN113190682B (zh) * 2021-06-30 2021-09-28 平安科技（深圳）有限公司 基于树模型的事件影响度获取方法、装置及计算机设备
CN116662572A (zh) * 2023-06-09 2023-08-29 平安银行股份有限公司 基于金融事理图谱的资产分析方法、装置、系统及介质

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2008120904B1 (en) * 2007-03-29 2008-11-27 J4Ad Co Ltd System for providing advertisements and method thereof
CN103049804A (zh) * 2013-01-15 2013-04-17 西安交通大学 一种基于量子力学和社交网络的股票价格趋势预测方法
CN103258034A (zh) * 2013-05-14 2013-08-21 江苏名通信息科技有限公司 一种基于社交媒体的经济金融行为分析系统模型
CN105373853A (zh) * 2015-11-18 2016-03-02 百度在线网络技术（北京）有限公司 股票舆情指数预测方法以及装置
CN105373854A (zh) * 2015-11-18 2016-03-02 百度在线网络技术（北京）有限公司 股票舆情指数预测方法以及装置

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120296845A1 (en) * 2009-12-01 2012-11-22 Andrews Sarah L Methods and systems for generating composite index using social media sourced data and sentiment analysis
US11257161B2 (en) * 2011-11-30 2022-02-22 Refinitiv Us Organization Llc Methods and systems for predicting market behavior based on news and sentiment analysis
CN102708153B (zh) * 2012-04-18 2015-05-06 中国信息安全测评中心 自适应在线社交网络热点话题发展趋势预测方法及系统
CN103559207A (zh) * 2013-10-10 2014-02-05 江苏名通信息科技有限公司 一种基于社交媒体计算的金融行为分析系统
CN105786962A (zh) * 2016-01-15 2016-07-20 优品财富管理有限公司 基于新闻传播力的大数据指数分析方法及系统

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2008120904B1 (en) * 2007-03-29 2008-11-27 J4Ad Co Ltd System for providing advertisements and method thereof
CN103049804A (zh) * 2013-01-15 2013-04-17 西安交通大学 一种基于量子力学和社交网络的股票价格趋势预测方法
CN103258034A (zh) * 2013-05-14 2013-08-21 江苏名通信息科技有限公司 一种基于社交媒体的经济金融行为分析系统模型
CN105373853A (zh) * 2015-11-18 2016-03-02 百度在线网络技术（北京）有限公司 股票舆情指数预测方法以及装置
CN105373854A (zh) * 2015-11-18 2016-03-02 百度在线网络技术（北京）有限公司 股票舆情指数预测方法以及装置

Also Published As

Publication number Publication date
CN107945034A (zh) 2018-04-20

Similar Documents

Publication Publication Date Title
CN112148987B (zh) 基于目标对象活跃度的消息推送方法及相关设备
CN111666346B (zh) 信息归并方法、交易查询方法、装置、计算机及存储介质
CN113360580B (zh) 基于知识图谱的异常事件检测方法、装置、设备及介质
US20210092160A1 (en) Data set creation with crowd-based reinforcement
AU2019232799A1 (en) Service processing method and apparatus
US20210035126A1 (en) Data processing method, system and computer device based on electronic payment behaviors
WO2019061994A1 (zh) 电子装置、保险产品推荐方法、系统及计算机可读存储介质
CN108416506B (zh) 客户风险等级管理方法、服务器及计算机可读存储介质
US11934290B2 (en) Interactive model performance monitoring
CN110135978B (zh) 用户金融风险评估方法、装置、电子设备和可读介质
WO2019061664A1 (zh) 电子装置、基于用户上网数据的产品推荐方法及存储介质
CN114090601B (zh) 一种数据筛选方法、装置、设备以及存储介质
CN114495137B (zh) 票据异常检测模型生成方法与票据异常检测方法
US11783206B1 (en) Method and system for making binary predictions for a subject using historical data obtained from multiple subjects
CN110428139A (zh) 基于标签传播的信息预测方法及装置
WO2019095569A1 (zh) 基于微博财经事件的金融分析方法、应用服务器及计算机可读存储介质
CN113722593A (zh) 事件数据处理方法、装置、电子设备和介质
CN116664306A (zh) 风控规则的智能推荐方法、装置、电子设备及介质
JP2020013229A (ja) 債務不履行確率を算出する装置、方法及びそのためのプログラム
CN113469696B (zh) 一种用户异常度评估方法、装置及计算机可读存储介质
CN111429257B (zh) 一种交易监控方法和装置
CN112241433A (zh) 产品的演示方法、装置、计算机设备及存储介质
CN114238993B (zh) 风险检测方法、装置、设备及介质
CN114925275A (zh) 产品推荐方法、装置、计算机设备及存储介质
CN113034294A (zh) 一种信贷风险展示方法和装置

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 18879238

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 29/09/2020)

122 Ep: pct application non-entry in european phase

Ref document number: 18879238

Country of ref document: EP

Kind code of ref document: A1