CN110825868A - Topic popularity based text pushing method, terminal device and storage medium - Google Patents
Topic popularity based text pushing method, terminal device and storage medium Download PDFInfo
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Abstract
The invention relates to a text pushing method based on topic popularity, a terminal device and a storage medium, wherein the method comprises the following steps: s1: collecting text data, and calculating a weight index of the text data; s2: calculating a title text vector and a content text vector of the text data according to the title and the content of the text data; s3: according to the topic corresponding to each text data stored in the topic database and the maximum value of the similarity between the text data and the text data stored in the topic database, obtaining the topic corresponding to the text data; s4: calculating the heat scores of the topics corresponding to the text data according to the parameters of the dimensions of the topics; s5: according to the heat scores of the topics corresponding to the text data and the heat score thresholds of different heat types, the text data are divided into the corresponding heat types, and the text data are pushed according to the heat types of the topics. The method and the device combine the text processing, topic discovery and other related technologies to improve the discovery capability of public opinion topics.
Description
Technical Field
The invention relates to the field of text popularity calculation, in particular to a text pushing method based on topic popularity, a terminal device and a storage medium.
Background
The network public opinion is a opinion and a comment which are transmitted through the Internet and have strong influence and tendency on some problems in real life by the public, is realized mainly through media such as news comments, forum postings, blogs, microblogs and the like, and intensively reflects the network public opinion in a time period. Generally, a public opinion monitoring system is a system for performing information acquisition, topic discovery, popularity assessment, tracking early warning and analysis processing on network public opinions, and the topic discovery is an important part in the public opinion monitoring system.
Disclosure of Invention
In view of the foregoing problems, the present invention aims to provide a text pushing method based on topic popularity, a terminal device and a storage medium.
The specific scheme is as follows:
a text pushing method based on topic popularity comprises the following steps:
s1: collecting text data, and calculating a weight index of the text data according to attributes corresponding to the text data and a weight coefficient corresponding to each attribute;
s2: calculating a title text vector and a content text vector of the text data according to the title and the content of the text data;
s3: according to the topic corresponding to each text data stored in the topic database and the maximum value of the similarity between the text data and the text data stored in the topic database, obtaining the topic corresponding to the text data;
s4: calculating the heat scores of the topics corresponding to the text data according to the parameters of the dimensions of the topics;
s5: according to the heat scores of the topics corresponding to the text data and the heat score thresholds of different heat types, the text data are divided into the corresponding heat types, and the text data are pushed according to the heat types of the topics.
Further, the calculation method of the weight index of the text data comprises the following steps:
W＝∑f(Ti,Ri)
wherein W represents a weight index, TiI-th attribute, R, representing text dataiAnd f represents a weight coefficient corresponding to the ith attribute of the text data, and a weight calculation function.
Further, the method for calculating the maximum value of the similarity in step S3 includes: extracting text data with the time difference between the storage time and the current time smaller than a time threshold from the topic database to serve as data to be compared, calculating the similarity between each piece of text data in the data to be compared and the collected text data, and extracting the maximum value of the similarity.
Further, the ith text data L in the data to be comparediSimilarity M to collected text data CiThe calculation formula of (2) is as follows:
Mi＝αF(WLi,WC)+βF(StLi,StC)+γF(ScLi,ScC)
wherein, WLiRepresenting text data LiWeight index of (1), WCWeight index, St, representing text data CLi、ScLiRespectively represent text data LiTitle text vector and content text vector of, StC、ScCThe title text vector and the content text vector respectively representing the text data C, α, β, and γ are respectively corresponding weight coefficients, and F represents a similarity calculation function.
Further, in step S3, when the maximum value of the similarity is greater than the similarity threshold, setting the topic of the collected text data as the topic corresponding to the text data in the topic database corresponding to the maximum value; otherwise, setting the topic of the collected text data as the topic of the text data.
Further, the calculation formula of the heat score of the topic in step S4 is:
P＝∑RDj*FDj(Dj)
wherein D isjParameter representing jth dimension of topic, FDjJ-th one representing topicFractional calculation function of dimension, RDjThe weight coefficient occupied by the jth dimension representing the topic.
Further, step S5 includes: setting a high heat score threshold value and a low heat score threshold value, taking the topic with the heat score larger than the high heat score threshold value as a high heat topic, taking the topic with the heat score smaller than the low heat score threshold value as a low heat topic, taking the rest topics as middle heat topics, and pushing the collected text data according to the type of the topic which is the high heat topic, the middle heat topic or the low heat topic.
A text pushing terminal device based on topic popularity comprises a processor, a memory and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to realize the steps of the method of the embodiment of the invention.
A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to an embodiment of the invention as described above.
According to the technical scheme, the public opinion topics are discovered by combining with text processing, topic discovery and other related technologies, the public opinion topic discovery capability is improved, and more valuable public opinion topic data is recommended according to the topic heat value calculated in multiple dimensions.
Drawings
Fig. 1 is a schematic flow chart according to a first embodiment of the present invention.
Detailed Description
To further illustrate the various embodiments, the invention provides the accompanying drawings. The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the embodiments. Those skilled in the art will appreciate still other possible embodiments and advantages of the present invention with reference to these figures.
The invention will now be further described with reference to the accompanying drawings and detailed description.
The first embodiment is as follows:
referring to fig. 1, the present invention provides a text pushing method based on topic popularity, including the following steps:
s1: and collecting text data, and calculating a weight index of the text data according to the attributes corresponding to the text data and the weight coefficient corresponding to each attribute.
In this embodiment, the text data is obtained by formatting the article/post information on the network collected by a tool such as an internet crawler.
The attributes of the text data may be: the website where the text data is located, the content classification of the text data, the content involvement place of the text data, the emotional tendency of the text data, the number of posts of the text data on the Internet and the like. The attributes set in this embodiment are main factors that influence the accuracy of topic discovery, and include three attributes, which are respectively expressed as: t is1: text data content classification weight factor, T2: text data content site-related weight factor, T3: and the text data emotion tendency weighting factor.
Because different attributes have different degrees of influence on data, the different attributes correspond to different weight coefficients, the weight coefficient range is set to be 0-9 in the embodiment, and the weight coefficients can be adjusted by a person skilled in the art in specific application according to actual conditions, in the embodiment, the weight coefficients corresponding to the different attributes are defined as: r1: article content classification weight coefficient, R2: article content site-related weight coefficient, R3: the emotion tendency weighting coefficient of the article.
Calculating the weight value w of each attribute according to each attribute and the corresponding weight coefficient thereofi：
wi＝f(Ti,Ri)
Wherein f represents a weight calculation function, TiWeight factor, R, representing an attributeiRepresenting the weight coefficient corresponding to the attribute.
Calculating a weight index W according to the weight value of each unique attribute:
W＝∑(wi)
s2: based on the title and content of the text data, a title text vector and a content text vector are calculated.
In this embodiment, a text vector is calculated by using a SimHash algorithm, and St is set to represent a header text vector and Sc is set to represent a content text vector. In other embodiments, those skilled in the art may also use other text vector calculation methods for calculation, which is not limited herein.
S3: and obtaining the topic corresponding to the text data according to the topic corresponding to each text data stored in the topic database and the maximum value of the similarity between the text data and the text data stored in the topic database.
In this embodiment, step S3 includes the steps of:
s301: setting a topic database T for storing text data and topics corresponding to the text data, judging whether data exist in the T, and if so, entering S302; otherwise, storing the acquired text data in the T, and setting the topic of the acquired text data as the topic of the text data.
In this embodiment, the topic itself is a topic of text data, and in other embodiments, the topic itself may be extracted according to other methods.
S302: extracting data with a time difference between the storage time stored in the T and the current time smaller than a time threshold (seven days in this embodiment), setting the extracted data to L, and setting each piece of data L in L to LiCalculating the similarity M between the text data C and the acquired text data Ci。
In this embodiment, the similarity MiThe calculation formula of (2) is as follows:
Mi＝αF(WLi,WC)+βF(StLi,StC)+γF(ScLi,ScC)
wherein, WLiRepresenting text data LiWeight index of (1), WCWeight index, St, representing text data CLi、ScLiRespectively represent text data LiTitle text vector and content text vector of, StC、ScCIndividual watchA title text vector and a content text vector of the text data C are shown, F represents a similarity calculation function, and those skilled in the art can select from the similarity calculation functions commonly used in the prior art based on experience, α, β, and γ respectively represent a correlation index similarity weight, a text title similarity weight, and a text content similarity weight, and in this embodiment, the value ranges of the three weights are set to be (0, 1).
S303: setting a similarity threshold value, and judging the similarity M of each piece of data in the L and the acquired text dataiMaximum value of (M)xWhether the similarity is greater than a similarity threshold value or not, if so, setting the topic of the acquired text data as a maximum value MxTopics corresponding to the x-th text data; otherwise, the collected text data does not belong to the existing topics, the collected text data is stored in the T, and the topics of the collected text data are set as the topics of the collected text data.
S4: calculating the heat scores of the topics corresponding to the text data according to the parameters of the dimensions of the topics;
in this embodiment, the heat score P of each topic is calculated by the formula
P＝∑RDj*FDj(Dj)
Wherein D isjParameter representing jth dimension of topic, FDjScore computation function, R, representing the jth dimension of a topicDjThe weight coefficient occupied by the jth dimension representing the topic.
In this embodiment, 8 dimensions are set, which are: the number of publishing websites, the number of publishing websites located in the home, the number of publishing websites located out of the home, the number of netizen forwarding, the number of netizen comments, the number of netizen praise, the number of sensitive words in the article, and the number of hit articles. The above-mentioned 8 dimensions are only one embodiment, and in other embodiments, the dimensions may be modified or increased or decreased according to the situation, and are not limited herein.
S5: according to the heat scores of the topics corresponding to the text data and the heat score thresholds of different heat types, the text data are divided into the corresponding heat types, and the text data are pushed according to the heat types of the topics.
In the embodiment, a high heat score threshold and a low heat score threshold are set, topics with heat scores greater than the high heat score threshold are used as high heat topics, topics with heat scores less than the low heat score threshold are used as low heat topics, other topics are used as medium heat topics, and collected text data are pushed according to the types of the topics which are high heat topics, medium heat topics or low heat topics.
The embodiment of the invention combines the text processing, topic discovery and other related technologies, improves the discovery capability of the public sentiment topics, and recommends more valuable public sentiment topic data according to the topic heat value calculated by multiple dimensions.
Example two:
the invention also provides text pushing terminal equipment based on topic popularity, which comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the processor executes the computer program to realize the steps of the method embodiment of the first embodiment of the invention.
Further, as an executable scheme, the text push terminal device based on the topic popularity may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The text pushing terminal device based on topic popularity can comprise, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above-mentioned constituent structure of the text push terminal device based on the topic popularity is only an example of the text push terminal device based on the topic popularity, and does not constitute a limitation on the text push terminal device based on the topic popularity, and may include more or less components than the above, or combine some components, or different components, for example, the text push terminal device based on the topic popularity may further include an input/output device, a network access device, a bus, and the like, which is not limited by the embodiment of the present invention.
Further, as an executable solution, the processor may be a Central Processing Unit (CPU), other general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, and the like. The general-purpose processor may be a microprocessor, or the processor may be any conventional processor, and the processor is a control center of the text push terminal device based on topic popularity, and various interfaces and lines are used to connect various parts of the whole text push terminal device based on topic popularity.
The memory can be used for storing the computer program and/or the module, and the processor realizes various functions of the text push terminal device based on topic popularity by running or executing the computer program and/or the module stored in the memory and calling the data stored in the memory. The memory can mainly comprise a program storage area and a data storage area, wherein the program storage area can store an operating system and an application program required by at least one function; the storage data area may store data created during the execution of the program, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The invention also provides a computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the above-mentioned method of an embodiment of the invention.
The text push terminal device integrated module/unit based on topic popularity can be stored in a computer readable storage medium if it is implemented in the form of a software functional unit and sold or used as an independent product. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM ), Random Access Memory (RAM), software distribution medium, and the like.
While the invention has been particularly shown and described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (9)
1. A text pushing method based on topic popularity is characterized in that: the method comprises the following steps:
s1: collecting text data, and calculating a weight index of the text data according to attributes corresponding to the text data and a weight coefficient corresponding to each attribute;
s2: calculating a title text vector and a content text vector of the text data according to the title and the content of the text data;
s3: according to the topic corresponding to each text data stored in the topic database and the maximum value of the similarity between the text data and the text data stored in the topic database, obtaining the topic corresponding to the text data;
s4: calculating the heat scores of the topics corresponding to the text data according to the parameters of the dimensions of the topics;
s5: according to the heat scores of the topics corresponding to the text data and the heat score thresholds of different heat types, the text data are divided into the corresponding heat types, and the text data are pushed according to the heat types of the topics.
2. The topic popularity-based text pushing method according to claim 1, wherein: the calculation method of the weight index of the text data comprises the following steps:
W＝∑f(Ti,Ri)
wherein W represents a weight index, TiI-th attribute, R, representing text dataiAnd f represents a weight coefficient corresponding to the ith attribute of the text data, and a weight calculation function.
3. The topic popularity-based text pushing method according to claim 1, wherein: the method for calculating the maximum value of the similarity in step S3 includes: extracting text data with the time difference between the storage time and the current time smaller than a time threshold from the topic database to serve as data to be compared, calculating the similarity between each piece of text data in the data to be compared and the collected text data, and extracting the maximum value of the similarity.
4. The topic popularity-based text pushing method according to claim 3, wherein: ith text data L in data to be comparediSimilarity M to collected text data CiThe calculation formula of (2) is as follows:
Mi＝αF(WLi,WC)+βF(StLi,StC)+γF(ScLi,ScC)
wherein, WLiRepresenting text data LiWeight index of (1), WCWeight index, St, representing text data CLi、ScLiRespectively represent text data LiTitle text vector and content text vector of, StC、ScCThe title text vector and the content text vector respectively representing the text data C, α, β, and γ are respectively corresponding weight coefficients, and F represents a similarity calculation function.
5. The topic popularity-based text pushing method according to claim 1, wherein: in step S3, when the maximum value of the similarity is greater than the similarity threshold, setting the topic of the collected text data as the topic corresponding to the text data in the topic database corresponding to the maximum value; otherwise, setting the topic of the collected text data as the topic of the text data.
6. The topic popularity-based text pushing method according to claim 1, wherein: the calculation formula of the heat score of the topic in step S4 is:
P＝∑RDj*FDj(Dj)
wherein D isjParameter representing jth dimension of topic, FDjScore computation function, R, representing the jth dimension of a topicDjThe weight coefficient occupied by the jth dimension representing the topic.
7. The topic popularity-based text pushing method according to claim 1, wherein: step S5 includes: setting a high heat score threshold value and a low heat score threshold value, taking the topic with the heat score larger than the high heat score threshold value as a high heat topic, taking the topic with the heat score smaller than the low heat score threshold value as a low heat topic, taking the rest topics as middle heat topics, and pushing the collected text data according to the type of the topic which is the high heat topic, the middle heat topic or the low heat topic.
8. The utility model provides a text propelling movement terminal equipment based on topic heat which characterized in that: comprising a processor, a memory and a computer program stored in said memory and running on said processor, said processor implementing the steps of the method according to any one of claims 1 to 7 when executing said computer program.
9. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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| CN113852692A (en) * | 2021-09-24 | 2021-12-28 | 中国移动通信集团陕西有限公司 | Service determination method, device, equipment and computer storage medium |
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