Application hot list data analysis method and device, equipment and storage medium

Abstract

The invention provides a hot list data analysis method and device of an application program, equipment and a storage medium, and relates to the field of financial science and technology or other related fields. According to the method, multiple pieces of target hot list data of multiple application programs are obtained, the target hot list data are processed to obtain multiple segmented words and the part-of-speech of each segmented word, and the target hot list data are classified according to the segmented words to obtain corresponding event classifications; therefore, the information related to the target industry is identified in the target hot list data. And determining an event theme of the same event corresponding to the event classification according to the part-of-speech of each segmented word in the event classification, and according to an emotion parameter of each segmented word in the event classification. Wherein the emotional parameters of the segmented words not only can represent emotional polarity corresponding to the segmented words, namely positive or negative, but also can represent emotional programs of the segmented words, so that the overall emotional parameters of the event can be reflected more accurately according to the emotional parameters of the segmented words, and the event can be understood more accurately.

Description

Technical Field

[0001] The present application relates to the field of financial technology or other related fields, and in particular to a method, device, equipment and storage medium for analyzing hot list data of an application. Background Art

[0002] With the continuous development of mobile Internet, the hot lists of various applications have become an important channel for people to obtain the latest information and hot topics. Therefore, monitoring the hot list data can provide important support for the public opinion risk management in various industries.

[0003] Existing monitoring schemes for hot list data are usually unable to accurately identify content information related to certain specified fields, so the accuracy of information screening is poor; and existing monitoring schemes only confirm the public's attitude towards events by identifying the emotional polarity of data texts, ignoring the subtle details of the public's emotional changes, so the understanding of hot events is not comprehensive and in-depth enough.

[0004] Therefore, the existing monitoring solutions for hot list data have defects in the accuracy of information screening and understanding when identifying relevant data information in a specified field. Summary of the invention

[0005] The present application provides a method, device, equipment and storage medium for analyzing the hot list data of an application program, so as to solve the technical problem that the existing monitoring scheme for hot list data has defects in the accuracy of information screening and information understanding when identifying relevant data information in a specified field.

[0006] In a first aspect, the present application provides a method for analyzing hot list data of an application, comprising:

[0007] Get multiple target hot list data from one or more applications;

[0008] Processing the target hot list data to obtain a plurality of segmented words and the part of speech of each segmented word;

[0009] Classifying the one or more target hot list data according to the segmentation to obtain at least one event classification, each of the event classifications including target hot list data of the same event;

[0010] Determine the event theme of the same event corresponding to the event classification according to the part of speech of each participle in the event classification;

[0011] According to the emotion parameters of each word segment in the event classification, the emotion parameters of the event theme corresponding to each event classification are determined, so as to process the event theme according to the emotion parameters of the event theme.

[0012] In a second aspect, the present application provides a device for analyzing hot list data of an application program, comprising:

[0013] An acquisition module, used for acquiring a plurality of target hot list data from one or more applications, and processing the target hot list data to obtain a plurality of segmented words and a part of speech of each segmented word;

[0014] A control module, configured to classify the one or more target hot list data according to the segmentation words to obtain at least one event classification, each of the event classifications including target hot list data of the same event;

[0015] A processing module, used to determine the event theme of the same event corresponding to the event classification according to the part of speech of each word in the event classification;

[0016] The execution module is used to determine the emotion parameters of the event theme corresponding to each event classification according to the emotion parameters of each word segment in the event classification, so as to process the event theme according to the emotion parameters of the event theme.

[0017] In a third aspect, the present application provides an electronic device, characterized in that it includes: a processor, and a memory communicatively connected to the processor;

[0018] The memory stores computer-executable instructions;

[0019] The processor executes the computer-executable instructions stored in the memory to implement the above-mentioned hot list data analysis method of the application.

[0020] In a fourth aspect, the present application provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores computer execution instructions, and when the computer execution instructions are executed by a processor, they are used to implement the hot list data analysis method of the application as described above.

[0021] In a fifth aspect, a computer program product is provided, characterized in that it comprises a computer program which, when executed by a processor, implements the above-mentioned method for analyzing the hot list data of the application.

[0022] The application's hot list data analysis method, device, equipment and storage medium provided in the present application obtain multiple target hot list data of multiple applications, process the target hot list data to obtain multiple segmented words and the part of speech of each segmented word, and classify each target hot list data according to the segmented words to obtain the corresponding event classification, thereby realizing the identification of information related to the target industry in the target hot list data. The event theme of the same event corresponding to the event classification is determined according to the part of speech of each segmented word in the event classification, and the emotional parameters of the event theme corresponding to each event classification are determined according to the emotional parameters of each segmented word in the event classification. The emotional parameters of the segmented words can not only represent the emotional polarity corresponding to the segmented words, that is, positive or negative, but also represent the emotional program of the segmented words. Therefore, the overall emotional parameters of the event can be more accurately reflected according to the emotional parameters of the segmented words, and the event can be understood more accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0024] Figure 1 Schematic diagram of the process of the hot list data analysis method of the application provided in the embodiment of the present application Figure 1 .

[0025] Figure 2 Schematic diagram of the process of the hot list data analysis method of the application provided in the embodiment of the present application Figure 2 .

[0026] Figure 3 A schematic diagram of the composition of the emotion dictionary provided in an embodiment of the present application.

[0027] Figure 4 A schematic diagram of a relationship diagram provided for an embodiment of the present application.

[0028] Figure 5 A schematic diagram of the emotion ratio provided in an embodiment of the present application.

[0029] Figure 6 A schematic diagram of the structure of the hot list data analysis device for the application provided in this application.

[0030] Figure 7 A schematic diagram of the structure of an electronic device provided in this application.

[0031] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0032] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0033] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with the relevant laws, regulations and standards of the relevant countries and regions, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0034] In addition, this application involves conducting big data analysis of user information (including but not limited to personal biometrics, identity data, consumption data, asset data, electronic terminal operation data, etc.), and using artificial intelligence technology to make automated decisions, and making technical solutions that have a significant impact on personal rights and interests based on the results of automated decisions. The application provides users with corresponding operation entrances for them to choose to agree or reject the results of automated decisions; if the user chooses to reject, the expert decision-making process will be entered.

[0035] It should be noted that the hot list data analysis method, device, equipment and storage medium of the application provided in this application can be used in the field of financial technology, and can also be used in any field other than the field of financial technology. The application field of the hot list data analysis method, device, equipment and storage medium of the application in this application is not limited.

[0036] With the continuous development of the Internet, data is also continuously accumulating. Extracting and deriving valuable data from a large amount of disorganized raw data is the technical direction promoted by various institutions and enterprises. For institutions in various industries, they can accurately identify information from massive data, conduct effective information mining, and explore and analyze public data such as hot events, and transform data value into knowledge application, so as to fully and deeply understand customers' attitudes and evaluations of products in various industries.

[0037] Among the public data on the Internet, the hot list data of various applications or websites has become an important channel for the public to obtain the latest information and hot topics. In various industries, the news data in these hot lists often reflect the public's attention and attitude towards related events. Therefore, monitoring the hot list data can provide strong support for public opinion risk management and product improvement in the industry fields that need attention.

[0038] In the prior art, there are schemes for monitoring hot list data, such as a method for detecting abnormal topics, which collects hot search list topic data from social media platforms as sample data, and marks the sample data with sentiment types. Based on the marked sample data, a feature determination rule model is trained and constructed, and then the abnormal topic groups in the hot list data are identified through the feature determination rule model.

[0039] An analysis of existing technical solutions shows that existing methods for monitoring hot list data are usually unable to accurately identify content related to a specified industry field, resulting in the monitoring results containing a large amount of irrelevant information, reducing the efficiency and accuracy of the analysis; existing methods for monitoring hot list data only identify the sentiment polarity of the text, such as positive, negative or neutral, and are unable to deeply analyze the public's attitude towards the event and subtle changes in emotions, resulting in an insufficiently comprehensive and in-depth understanding of hot events. In addition, existing methods for monitoring hot list data lack a timely early warning mechanism for negative events, and are unable to detect and respond in a timely manner, which may lead to the occurrence of a public opinion crisis.

[0040] The hot list data analysis method, device, equipment and storage medium of the application provided in this application are intended to solve the above technical problems of the prior art.

[0041] The application's hot list data analysis method provided in the present application obtains multiple target hot list data of multiple applications, processes the target hot list data to obtain multiple segmentations and the part of speech of each segmentation, and classifies each target hot list data according to the segmentation to obtain the corresponding event classification, thereby realizing the identification of information related to the target industry in the target hot list data. The event theme of the same event corresponding to the event classification is determined according to the part of speech of each segmentation in the event classification, and the emotional parameters of the event theme corresponding to each event classification are determined according to the emotional parameters of each segmentation in the event classification. The emotional parameters of the segmentation can not only represent the emotional polarity corresponding to the segmentation, that is, positive or negative, but also represent the emotional program of the segmentation. Therefore, the overall emotional parameters of the event can be more accurately reflected according to the emotional parameters of the segmentation, and the event can be understood more accurately.

[0042] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0043] Figure 1 Schematic diagram of the process of the hot list data analysis method of the application provided in the embodiment of the present application Figure 1 ,like Figure 1 As shown, the method includes:

[0044] S101. Acquire multiple target hot list data from one or more applications, and process the target hot list data to obtain multiple segmented words and the part of speech of each segmented word.

[0045] Specifically, a request is sent to each application or website with a hot list function to capture the hot list data of each application, and then the hot list data is pre-processed to remove irrelevant content such as web links and spaces in the hot list data.

[0046] Further, according to the target industry information input by the user, the hot list data related to the target industry information is confirmed in the multiple processed hot list data as the target hot list data. The Chinese word segmenter is used to segment and tag the target hot list data, and multiple segmentations of the target hot list data and the part of speech of each segmentation are obtained, wherein the part of speech is used to indicate whether the corresponding segmentation is a noun, an adjective, a verb or others.

[0047] S102: Classify the one or more target hot list data according to the word segmentation to obtain at least one event classification, each of the event classifications including target hot list data of the same event.

[0048] Specifically, after obtaining multiple segmented words of each target hot list data and the part of speech of each segmented word, the target hot list data corresponding to each segmented word is clustered based on each segmented word through a clustering algorithm, so as to classify each target hot list data to obtain multiple clustering clusters. Each clustering cluster includes at least one target hot list data, and different clustering clusters correspond to different event classifications, that is, each event classification includes the target hot list data of the same event.

[0049] S103: Determine the event theme of the same event corresponding to the event classification according to the part of speech of each participle in the event classification.

[0050] Specifically, after completing the classification of each target hot list data and obtaining the event classification corresponding to different target hot list data, in each event classification, based on the part of speech of the corresponding word segment of the target hot list data, the part of speech is analyzed through named entity identification to determine the event theme corresponding to each event classification, such as the name of the organization or product name in the event.

[0051] S104: Determine the emotion parameters of the event topics corresponding to each event classification according to the emotion parameters of each word segment in the event classification, so as to process the event topics according to the emotion parameters of the event topics.

[0052] Specifically, after determining the event theme corresponding to each event classification, the emotional tendency parameters of the corresponding target hot list data are obtained according to the emotional parameters of the word segmentation of each target hot list data under each event classification, and the corresponding event classification emotional tendency parameters are determined according to the emotional tendency parameters of each target hot list data under the event classification, and the emotional tendency parameters of the event classification are used as the emotional parameters of the corresponding event theme. The emotional parameters of the event theme are detected in real time, and when the emotional parameters are greater than or equal to the threshold, prompt information is generated according to the event theme to warn the backend personnel.

[0053] The hot list data analysis method of the application provided in this embodiment obtains multiple target hot list data of one or more applications based on the target industry determined by the user, so as to realize the identification of information related to the target industry in the target hot list data. By classifying the parts of speech in the target hot list data to obtain at least one event classification, the event theme of the same event corresponding to the event classification is determined according to the parts of speech of each segmentation in the event classification, and the emotional parameters of the event theme corresponding to each event classification are determined according to the emotional parameters of each segmentation in the event classification. The emotional parameters of the segmentation can not only represent the emotional polarity corresponding to the segmentation, that is, positive or negative, but also represent the emotional program of the segmentation. Therefore, the overall emotional parameters of the event can be more accurately reflected according to the emotional parameters of the segmentation, and the event can be understood more accurately.

[0054] Figure 2 Schematic diagram of the process of the hot list data analysis method of the application provided in the embodiment of the present application Figure 2 , Figure 3 A schematic diagram of the composition of the emotion dictionary provided in the embodiment of the present application, Figure 4 A schematic diagram of a relationship diagram provided in an embodiment of the present application, Figure 5 The emotion ratio diagram provided in the embodiment of this application is combined with Figure 2-Figure 5 As shown, based on Figure 1 In an embodiment, the method comprises:

[0055] S201. Acquire multiple hot list data from one or more applications.

[0056] Specifically, the hot list webpage information of the application or website is obtained through the developer mode of the application or website, and the hot list data request of the application is constructed according to the hot list webpage information, that is, the request is obtained, and the hot list data request is sent to each application or website to obtain the hot list data of the application. In this way, the hot list data in the hot list of each application or website is obtained, wherein the hot list data includes information such as the list title, content, date, source and ranking.

[0057] Furthermore, the captured hot list data are preprocessed, that is, the fields in each hot list data such as title, text, time, author and source are parsed and extracted, and the web links, spaces, etc. in the text, as well as redundant irrelevant content such as forwarding information are removed through regular expressions. At the same time, the duplicate values ​​and empty values ​​in each hot list data are deleted, that is, redundant characters such as web links, spaces and duplicate characters are removed from the hot list data, so as to realize the preprocessing of the hot list data and improve the efficiency of subsequent data analysis and processing.

[0058] S202: Acquire target industry information input by the user, and acquire keywords related to the target industry as target keywords according to the target industry information.

[0059] Specifically, based on the target industry information input by the user, such as financial industry content, corresponding keywords are obtained in the content database, wherein different industry names and corresponding keyword groups are pre-associated and stored in the content database as corresponding target keywords. Of course, the keyword groups in the content database can be added, modified or deleted according to user customization.

[0060] S203: extract one or more target hot list data related to the target keyword from the hot list data.

[0061] Specifically, after obtaining the target keyword, based on the Natural Language Processing (NLP) solution, one or more keywords and phrases related to the target keyword are extracted from the processed hot list data through the bag-of-words model or the term frequency-inverse document frequency (TF-IDF) solution as the target hot list data to filter out invalid content.

[0062] S204: construct a custom dictionary to process the target hot list data through the custom dictionary to obtain a plurality of segmented words.

[0063] Specifically, after building the corresponding custom dictionary based on the proper nouns or special words in the industry field indicated by the target industry information, the Chinese word segmenter Jieba is used to segment and annotate the target hot list data based on the loaded corpus and custom dictionary, so as to provide the corresponding segmentation and annotated part-of-speech basis for the target hot list data for subsequent event classification.

[0064] S205: Classify the one or more target hot list data according to the segmentation words to obtain at least one event classification.

[0065] Specifically, after completing the word segmentation and part-of-speech tagging of each target hot list data, each target hot list data needs to be classified. Since the content types of each event on each hot list vary greatly, and the differences between the same types are small, the unsupervised clustering algorithm K-means is used as the classification algorithm.

[0066] Furthermore, the target keywords of the industry fields that need to be paid attention to indicated by the target industry information are vectorized through the word2vec (word to vector) model of Word emdedding, and the K-means model is called to cluster the segmented words, so as to classify the target hot list data corresponding to each segmented word to obtain the clustered clusters, so that each cluster corresponds to a different event classification, wherein each event classification includes at least one target hot list data.

[0067] S206: Determine the event theme of the same event corresponding to the event classification according to the part of speech of each participle in the event classification.

[0068] Specifically, since the entity relationships between events on each hot list cannot be directly observed, after obtaining at least one event classification, the pipeline serial extraction algorithm is used to perform named entity identification on the target hot list data that has been marked with parts of speech. For example, when the segmentation word of the target hot list data is an institution name, the corresponding part of speech is noun, and the segmentation word whose noun is the institution name is marked with a named entity.

[0069] Furthermore, the word segmentation of the named entity identifier is used as the corresponding event subject to extract the entities in the event classification, extract the relationship between the entities in the event classification, and generate the event topic of the corresponding event classification based on the relationship between the entities and the entities.

[0070] S207: Match each segmentation in the event classification with a pre-constructed emotion dictionary to obtain an emotion parameter of each segmentation, wherein the emotion dictionary includes emotion parameters corresponding to a plurality of emotion words.

[0071] Specifically, after determining the event topic corresponding to each event classification, for each segmented word in each target hot list data in each event classification, the segmented word is matched with a pre-constructed sentiment dictionary to obtain the sentiment parameter of the segmented word. The sentiment dictionary pre-associates and stores sentiment parameters corresponding to different segmented words. For example, when the segmented word is a positive word, the corresponding sentiment parameter is two, and when the segmented word is a negative word, the corresponding sentiment parameter is negative two.

[0072] Among them, refer to Figure 3 As shown, the emotion dictionary includes the basic emotion dictionary, the network emotion dictionary and the domain emotion dictionary. The basic emotion dictionary is used to indicate general emotions such as happy or painful, in which case happy is a commendatory word; the network emotion dictionary is used to indicate network terms such as awesome, in which case awesome is a commendatory word; the domain emotion dictionary is used to indicate situations in the target domain such as daily limit or bad news, in which case daily limit is a commendatory word and bad news is a derogatory word.

[0073] Furthermore, when it is necessary to determine the emotional tendency of sentences in event classification, for each word segment in each event data, the word segment is matched with the emotional words in a pre-constructed emotional dictionary to obtain the emotional parameters of the word segment, and the emotional parameters of the word segment included in each sentence in each event data are summed to obtain the emotional parameters of each sentence. Based on the emotional parameters of each sentence, the corresponding emotional tendency is determined according to the emotional parameters of each sentence, thereby realizing sentence-level content emotion recognition and improving recognition granularity.

[0074] S208: For each event classification, average the emotion parameters of each word in the event classification to obtain the emotion parameter of the event theme of the event classification.

[0075] Specifically, the sum of the emotion parameters of each word in the target hot list data is obtained as the emotion tendency parameter of the target hot list data. For example, when the sum is greater than zero, the emotion tendency is positive, when the sum is less than zero, the emotion tendency is negative, or when the sum is equal to zero, the emotion tendency is neutral.

[0076] Furthermore, the emotional tendency parameter of the target hot list data with positive emotional tendency is 1, the emotional tendency parameter of the target hot list data with negative emotional tendency is -1, and the emotional tendency parameter of the target hot list data with neutral emotional tendency is 0. The average emotional tendency parameter of each target hot list data in the event classification is obtained as the emotional parameter of the event theme of the event classification.

[0077] S209: When the emotion parameter of the event theme is greater than or equal to the warning threshold, issue a warning for the event theme and generate a processing plan.

[0078] Specifically, after obtaining the emotional parameters of the event themes of each event classification, it is detected whether the emotional parameters of each event theme are greater than or equal to the negative emotional warning threshold, and prompt information is generated according to the event theme and the corresponding emotional parameters to alert the backend personnel.

[0079] Among them, refer to Figure 4 Shown and Figure 5 As shown, the prompt information includes the relationship map diagram of each entity in the event theme, the sentiment ratio diagram, the data high-frequency statistics diagram, the media activity and information data information and the hot spot diagram. Among them, the relationship map diagram is used to indicate the transaction relationship between entities such as an institution and various company entities, and the sentiment ratio diagram is used to indicate the public's sentiment tendency towards the event theme, such as positive, neutral or negative.

[0080] Furthermore, when the emotional parameters of the event theme are not greater than the negative emotional warning threshold, the event theme is warned and a treatment plan is generated based on the change trend of the emotional parameters of the event theme, and the treatment plan is fed back when the backend personnel need to retrieve the data information corresponding to the event theme. By automatically collecting and analyzing the hot list data of mainstream applications and websites, the efficiency of data monitoring is improved, the dependence on manual operations is reduced, and it can be displayed in actual applications, reducing false positives and false negatives and providing statistical data comparison of accuracy.

[0081] The hot list data analysis method of the application provided in this embodiment obtains multiple target hot list data of one or more applications based on the target industry determined by the user, so as to realize the identification of information related to the target industry in the target hot list data. By classifying the parts of speech in the target hot list data to obtain at least one event classification, the event theme of the same event corresponding to the event classification is determined according to the parts of speech of each segmentation in the event classification, and the emotional parameters of the event theme corresponding to each event classification are determined according to the emotional parameters of each segmentation in the event classification. The emotional parameters of the segmentation can not only represent the emotional polarity corresponding to the segmentation, that is, positive or negative, but also represent the emotional program of the segmentation. Therefore, the overall emotional parameters of the event can be more accurately reflected according to the emotional parameters of the segmentation, and the event can be understood more accurately.

[0082] Figure 6 The schematic diagram of the structure of the hot list data analysis device 60 for the application provided in the present application includes: an acquisition module 601, a control module 602, a processing module 603 and an execution module 604, wherein:

[0083] The acquisition module 601 is used to acquire multiple target hot list data from one or more applications, and process the target hot list data to obtain multiple segmented words and the part of speech of each segmented word;

[0084] A control module 602 is used to classify the one or more target hot list data according to the segmentation to obtain at least one event classification, each of which includes target hot list data of the same event;

[0085] Processing module 603, used to determine the event theme of the same event corresponding to the event classification according to the part of speech of each word in the event classification;

[0086] The execution module 604 is used to determine the emotion parameters of the event theme corresponding to each event classification according to the emotion parameters of each segmentation in the event classification, so as to process the event theme according to the emotion parameters of the event theme.

[0087] In a possible implementation, the execution module 604 is specifically configured to issue an early warning to the event subject and generate a processing plan when the emotion parameter of the event subject is greater than or equal to the early warning threshold;

[0088] And / or, based on the change trend of the emotional parameters of the event theme, an early warning is issued for the event theme and a processing plan is generated.

[0089] In a possible implementation, the execution module 604 is specifically configured to match each segmentation in each event data with an emotion word in a pre-constructed emotion dictionary to obtain an emotion parameter of the segmentation;

[0090] The emotion parameters of the word segments included in each sentence in each event data are summed up to obtain the emotion parameter of each sentence.

[0091] In a possible implementation, the execution module 604 is specifically used to match each segmentation in the event classification with a pre-built emotion dictionary to obtain an emotion parameter of each segmentation, wherein the emotion dictionary includes emotion parameters corresponding to a plurality of emotion words;

[0092] For each of the event categories, the emotion parameters of the individual word segments in the event category are averaged to obtain the emotion parameters of the event theme of the event category.

[0093] In a possible implementation, the acquisition module 601 is specifically configured to construct a custom dictionary, so as to process the target hot list data through the custom dictionary to obtain a plurality of segmented words.

[0094] In a possible implementation, the acquisition module 601 is specifically used to acquire multiple hot list data from one or more applications;

[0095] Acquire target industry information input by a user, and acquire keywords related to the target industry as target keywords according to the target industry information;

[0096] One or more target hot list data related to the target keyword are extracted from the hot list data.

[0097] In a possible implementation, the acquisition module 601 is specifically configured to acquire the hot list web page information of the application program through the developer mode of the APP;

[0098] Constructing a hot list data request for the application according to the hot list webpage information;

[0099] Send the hot list data request to the application to obtain the hot list data of the application.

[0100] In a possible implementation, the acquisition module 601 is specifically configured to remove redundant characters from the hot list data, where the redundant characters include at least one of the following: a web page link, a space, and a repeated character.

[0101] The hot list data analysis device for applications provided in this embodiment can execute the method provided in the above method embodiment, and its implementation principle and technical effect are similar, which will not be described in detail in this embodiment.

[0102] Figure 7 The structural diagram of the electronic device provided in this application. Figure 7 As shown, the electronic device 7 provided in this embodiment includes: at least one processor 701 and a memory 702. Optionally, the device 70 further includes a communication component 703. The processor 701, the memory 702 and the communication component 703 are connected via a bus 704.

[0103] In a specific implementation process, at least one processor 701 executes the computer-executable instructions stored in the memory 702, so that at least one processor 701 executes the above method.

[0104] The specific implementation process of the processor 701 can be found in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be repeated here.

[0105] In the above embodiments, it should be understood that the processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the invention can be directly implemented as a hardware processor, or can be implemented by a combination of hardware and software modules in the processor.

[0106] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (NVM), such as at least one disk storage.

[0107] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of this application is not limited to only one bus or one type of bus.

[0108] The present application also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0109] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above method is implemented.

[0110] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0111] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present application.

[0112] It should be further noted that, although the various steps in the flowchart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowchart may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0113] It should be understood that the above-mentioned device embodiments are only illustrative, and the device of the present application can also be implemented in other ways. For example, the division of units / modules in the above-mentioned embodiments is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units, modules or components can be combined, or can be integrated into another system, or some features can be ignored or not executed.

[0114] In addition, unless otherwise specified, each functional unit / module in each embodiment of the present application may be integrated into one unit / module, each unit / module may exist physically separately, or two or more units / modules may be integrated together. The above-mentioned integrated unit / module may be implemented in the form of hardware or in the form of a software program module.

[0115] If the integrated unit / module is implemented in the form of hardware, the hardware may be a digital circuit, an analog circuit, etc. The physical implementation of the hardware structure includes but is not limited to transistors, memristors, etc. Unless otherwise specified, the processor may be any appropriate hardware processor, such as a CPU, a GPU, an FPGA, a DSP, an ASIC, etc. Unless otherwise specified, the storage unit may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory (RRAM), a dynamic random access memory (DRAM), a static random access memory (SRAM), an enhanced dynamic random access memory (EDRAM), a high-bandwidth memory (HBM), a hybrid memory cube (HMC), etc.

[0116] In the above embodiments, the description of each embodiment has its own emphasis. For the part not described in detail in a certain embodiment, please refer to the relevant description of other embodiments. The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0117] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0118] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for analyzing hot list data of an application, characterized in that: include: Get multiple target hot list data from one or more applications; Processing the target hot list data to obtain a plurality of segmented words and the part of speech of each segmented word; Classifying the one or more target hot list data according to the segmentation to obtain at least one event classification, each of the event classifications including target hot list data of the same event; Determine the event theme of the same event corresponding to the event classification according to the part of speech of each participle in the event classification; According to the emotion parameters of each word segment in the event classification, the emotion parameters of the event theme corresponding to each event classification are determined, so as to process the event theme according to the emotion parameters of the event theme.

2. The method according to claim 1, characterized in that The processing of the event theme according to the emotion parameter of the event theme includes: When the emotional parameter of the event subject is greater than or equal to the warning threshold, a warning is issued for the event subject and a processing plan is generated; And / or, based on the change trend of the emotional parameters of the event theme, an early warning is issued for the event theme and a processing plan is generated.

3. The method according to claim 1, characterized in that Determining the sentiment parameter of each word segmentation includes: For each segmentation in each event data, the segmentation is matched with an emotional word in a pre-constructed emotional dictionary to obtain an emotional parameter of the segmentation; The emotion parameters of the word segments included in each sentence in each event data are summed up to obtain the emotion parameter of each sentence.

4. The method according to claim 1, characterized in that: Determining the emotion parameters of the event theme corresponding to each event classification according to the emotion parameters of each segmentation in the event classification includes: Matching each segmentation in the event classification with a pre-constructed emotion dictionary to obtain an emotion parameter of each segmentation, wherein the emotion dictionary includes emotion parameters corresponding to a plurality of emotion words; For each of the event categories, the emotion parameters of the individual word segments in the event category are averaged to obtain the emotion parameters of the event theme of the event category.

5. The method according to claim 1, characterized in that The processing of the target hot list data to obtain a plurality of segmented words includes: A custom dictionary is constructed to process the target hot list data through the custom dictionary to obtain a plurality of segmented words.

6. The method according to any one of claims 1 to 5, characterized in that The step of obtaining multiple target hot list data from one or more applications includes: Get multiple hot list data from one or more applications; Acquire target industry information input by a user, and acquire keywords related to the target industry as target keywords according to the target industry information; One or more target hot list data related to the target keyword are extracted from the hot list data.

7. The method according to claim 6, characterized in that The step of obtaining multiple hot list data from one or more applications includes: Obtain the hot list web page information of the application program through the developer mode of the APP; Constructing a hot list data request for the application according to the hot list webpage information; Send the hot list data request to the application to obtain the hot list data of the application.

8. The method according to claim 6, characterized in that Before extracting one or more target hot list data related to the target keyword from the hot list data, the method further includes: Redundant characters are removed from the hot list data, where the redundant characters include at least one of the following: web page links, spaces, and repeated characters.

9. A device for analyzing hot list data of an application, characterized in that: include: An acquisition module, used for acquiring a plurality of target hot list data from one or more applications, and processing the target hot list data to obtain a plurality of segmented words and a part of speech of each segmented word; A control module, configured to classify the one or more target hot list data according to the segmentation words to obtain at least one event classification, each of the event classifications including target hot list data of the same event; A processing module, used to determine the event theme of the same event corresponding to the event classification according to the part of speech of each word in the event classification; The execution module is used to determine the emotion parameters of the event theme corresponding to each event classification according to the emotion parameters of each word segment in the event classification, so as to process the event theme according to the emotion parameters of the event theme.

10. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 8 when executed by a processor.

12. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 8 when being executed by a processor.

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