A network public opinion heat calculation and early warning system and method

Abstract

This invention discloses a system and method for calculating and warning about the popularity of online public opinion, belonging to the field of public opinion analysis technology. The method includes the following steps: S1, determining the public opinion object and its corresponding time domain for popularity calculation; S2, constructing basic popularity metrics using the public opinion object and time domain; S3, constructing popularity status metrics using the basic popularity metrics; S4, constructing popularity change description results using the popularity status results; S5, using the popularity change results to make early warning judgments. This invention, by setting up a set of basic popularity metrics, ensures that public opinion popularity calculation is based on a clearly defined public opinion object and time domain. It breaks down and standardizes the composition of public opinion popularity, allowing the scale of public opinion dissemination, the level of interactive activity, and the breadth of participation to be separately characterized and uniformly output, thereby effectively reducing interference caused by differences in data standards between different online platforms and different public opinion events.

Description

Technical Field

[0001] This invention relates to the field of public opinion analysis technology, specifically to a system and method for calculating and issuing early warnings of online public opinion intensity. Background Technology

[0002] With the rapid development of online media, social platforms, and information aggregation services, online public opinion has become an important vehicle for reflecting social concerns and changes in public sentiment. Existing online public opinion analysis technologies typically revolve around keyword retrieval, topic aggregation, or sentiment identification, using statistics on information dissemination volume, click volume, or sentiment polarity to make basic judgments about the public opinion situation. However, with the diversification of information dissemination channels and the increasing complexity of user participation methods, single indicators or empirical rules are no longer sufficient to accurately depict the true level of public opinion and its changing characteristics within a specific time frame.

[0003] In existing technologies, the calculation of public opinion heat is mostly based on single or simple combinations of indicators such as the number of information releases, the number of interactions, or the scale of users. There is a lack of systematic breakdown and unified expression of the heat components under the dual constraints of the public opinion object and the time domain. This makes it difficult to compare the heat results between different public opinions and makes it difficult to provide a stable and consistent basic input for subsequent heat status analysis and early warning judgment, thus affecting the reliability and interpretability of the early warning results. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a system and method for calculating and issuing early warnings of online public opinion trends, thereby resolving the problems mentioned in the background section.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] In a first aspect, embodiments of the present invention provide a method for calculating and issuing early warnings of online public opinion intensity, comprising the following steps:

[0007] S1. Determine the public opinion object and its corresponding heat calculation time domain;

[0008] S2. Construct basic representations of popularity using public opinion objects and time domains to obtain a set of basic representations of popularity.

[0009] S3. Construct a heat state quantity using the basic heat characterization quantity to obtain the public opinion heat state result;

[0010] S4. Use the public opinion heat status results to construct a description of heat change results, and obtain the public opinion heat change results;

[0011] S5. Use the results of changes in public opinion intensity to make early warning judgments and obtain public opinion intensity early warning results.

[0012] To further optimize this technical solution, step S1, through the analysis and determination of the public opinion object and the analysis and determination of the heat calculation time domain, clarifies the unique public opinion object and the corresponding heat calculation time domain targeted by subsequent public opinion heat calculation and early warning analysis, and obtains the following result: ,in:

[0013] This indicates the results of the analysis and identification of public opinion targets;

[0014] This indicates the time domain for calculating the popularity of the aforementioned public opinion object.

[0015] To further optimize this technical solution, step S2, under the premise that step S1 has completed the unique identification of the public opinion object and the time domain limitation of its heat calculation, transforms the originally scattered and incomparable online public opinion behavior into a structured, quantifiable, and technically meaningful basic heat characterization quantity.

[0016] Step S2, in constructing the basic characterization of heat, includes the following steps:

[0017] Based on the statistical analysis of basic behavioral data limited by the target of public opinion and the time domain;

[0018] Construct basic characterization metrics for popularity based on dissemination scale;

[0019] Construct basic representations of interactive activity levels for popularity;

[0020] Construct basic characterization metrics for participation breadth-based popularity;

[0021] The final output of step S2 is a set of fundamental thermal characteristics, expressed as follows:

[0022] .

[0023] To further optimize this technical solution, in step S2, when performing basic behavior volume statistics based on public opinion objects and time domain limitations, the public opinion object identification results output in step S1 are used. As an object constraint, heat is used to calculate the time domain; As a time constraint, network behavior measurement and log statistics techniques are used to statistically analyze information behaviors directly related to the public opinion object in cyberspace, and the following basic statistical quantities are determined:

[0024] In the time domain Inside, and marked as The cumulative number of information releases related to the public opinion target, including the public opinion target identifier. The public opinion object identification result output in step S1 Symbolic representation;

[0025] The cumulative number of interactive behaviors generated in response to the published information within the same time domain;

[0026] The number of different users who participated in the aforementioned posting or interaction within the time domain.

[0027] To further optimize this technical solution, in step S2, when constructing the basic representation quantity of the spread scale type of popularity, a basic representation quantity for representing the spread scale of public opinion is constructed based on the information release statistics. Its expression is:

[0028]

[0029] in, This indicates the scale of public opinion objects that are explicitly published and mentioned within the time domain of popularity calculation.

[0030] To further optimize this technical solution, in step S2, when constructing the basic representation of interactive activity level, a basic representation reflecting the level of public opinion dissemination activity is constructed based on the statistical results of interactive behavior. The interactive activity representation is as follows:

[0031] ;

[0032] Among them, molecules This indicates that public opinion-related information is in the time domain. The total amount of interactive behavior triggered within, denominator This indicates the scale of the information release.

[0033] To further optimize this technical solution, in step S2, when constructing the basic representation quantity of the breadth of participation in the popularity category, a basic representation quantity reflecting the scope of public opinion influence is constructed by quantifying the scale of participating entities. Its expression is as follows:

[0034] ;

[0035] in, This indicates the number of different users who participated in the release or interaction of information related to the public opinion object within the time domain of the popularity calculation.

[0036] To further optimize this technical solution, step S3 is based on the set of basic thermal characterization quantities output in step S2. Construct a structured, computable, and transferable representation of public opinion heat status to transform discrete basic heat status representations into a unified heat status description form;

[0037] Step S3 ultimately outputs a public opinion heat state vector, the expression of which is:

[0038]

[0039] in:

[0040] :Depend on The scale thermal state component obtained after uniform scale mapping;

[0041] :Depend on The active heat state component obtained after uniform scale mapping;

[0042] :Depend on The propagation breadth and heat state components obtained after uniform scale mapping.

[0043] To further optimize this technical solution, step S4 introduces a time dimension based on the obtained public opinion heat status results, and descriptively constructs the changes in public opinion heat status over time, thereby transforming the static heat status results formed in step S3 into heat change results that can reflect the development process of public opinion.

[0044] Step S5 determines the degree of abnormality of the change in public opinion heat based on the results of the change in public opinion heat obtained in step S4, and generates a public opinion heat warning result that can be directly applied.

[0045] A system for calculating and warning about the popularity of online public opinion includes the following modules: a module for identifying public opinion objects and determining the time domain, a module for calculating basic characteristics of popularity, a module for constructing the popularity status of public opinion, a module for analyzing changes in popularity of public opinion, and a module for judging the early warning of popularity of public opinion.

[0046] In a second aspect, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, they implement the steps of a system and method for calculating and warning about the popularity of online public opinion as described in the first aspect of the present invention.

[0047] Thirdly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, they implement the steps of a system and method for calculating and warning about the popularity of online public opinion as described in the first aspect of the present invention.

[0048] Compared with existing technologies, this invention provides a system and method for calculating and warning about the popularity of online public opinion, which has the following beneficial effects:

[0049] This system and method for calculating and early warning of online public opinion heat, by constructing a set of basic characterizing metrics, moves beyond the direct use of single statistical indicators. Instead, it breaks down and standardizes the composition of public opinion heat based on a clear understanding of the target audience and time domain. This allows for the separate characterization and unified output of the scale of public opinion dissemination, the level of interaction activity, and the breadth of participation, effectively reducing interference caused by differences in data standards across different online platforms and public opinion events. By forming structured and stable basic characterizing results, it not only improves the comparability and consistency of public opinion heat calculation results but also provides a clear and reliable input foundation for subsequent construction of public opinion heat status and analysis of heat change. Attached Figure Description

[0050] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 This is a flowchart illustrating a method for calculating and issuing early warnings of online public opinion intensity proposed in this invention.

[0052] Figure 2 This is a schematic diagram illustrating the process of constructing the basic characterization quantity of the heat index for a method of calculating and warning the heat index of online public opinion proposed in this invention.

[0053] Figure 3 This is a schematic diagram illustrating the construction process of a heat state quantity for a method for calculating and warning the heat of online public opinion proposed in this invention.

[0054] Figure 4 This is a schematic diagram of the early warning determination process for a method for calculating and warning the popularity of online public opinion proposed in this invention. Detailed Implementation

[0055] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0056] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0057] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0058] Example 1:

[0059] Reference Figures 1-4 This is the first embodiment of the present invention, which provides a method for calculating and issuing early warnings for online public opinion heat, including the following steps:

[0060] S1. Determine the public opinion object and its corresponding heat calculation time domain;

[0061] Step S1 clarifies the unique public opinion object and the corresponding time domain for subsequent public opinion heat calculation and early warning analysis.

[0062] Step S1, during its execution, includes the analysis and determination of the public opinion target and the analysis and determination of the time domain for heat calculation, wherein:

[0063] Analysis and identification of public opinion targets:

[0064] Based on a pre-defined set of network information carriers, existing mature keyword retrieval and topic identification technologies are used to retrieve and aggregate information content in cyberspace, forming a candidate set of public opinion objects related to the target event or topic, and generating a unique identifier number or code corresponding to each public opinion object, forming a public opinion object identifier lookup table.

[0065] The keyword retrieval is based on text content matching, and the topic recognition is based on text topic aggregation or existing topic labeling results.

[0066] Based on the candidate set, mature semantic similarity analysis technology is used to judge the semantic similarity of the text content of the candidate information, and keyword merging technology is combined to unify synonymous or highly related keywords, thereby merging multiple candidate objects pointing to the same event or topic into a single public opinion object identifier.

[0067] After the public opinion object has been uniquely processed, its validity is confirmed by mature information relevance and time continuity determination technology. This confirms that the public opinion object has relevant information in multiple consecutive time periods, thus meeting the basic requirement of public opinion continuity for subsequent heat calculation.

[0068] Analysis and determination of the time domain for heat calculation:

[0069] Based on the time point when the public opinion object first appeared and the current analysis time point, a mature time window segmentation technology is used to initially define the time range covered by the heat calculation.

[0070] To ensure the comparability of subsequent heat status results over time, mature time series standardization processing technology is used to uniformly set the analysis time granularity within the time range, so that the heat status results for each time period in subsequent steps are generated based on the same time length.

[0071] The start and end times of the heat calculation time domain are clearly defined to form a closed time analysis interval, ensuring that all heat-related calculations in subsequent steps are completed within this time domain.

[0072] The final result of step S1 is: ,in,

[0073] This indicates the results of the analysis and identification of public opinion targets;

[0074] This indicates the time domain for calculating the popularity of the aforementioned public opinion object.

[0075] S2. Construct basic representations of popularity using public opinion objects and time domains to obtain a set of basic representations of popularity.

[0076] Step S2, under the premise that step S1 has completed the unique identification of the public opinion object and the time domain limitation of its heat calculation, transforms the originally scattered and incomparable online public opinion behavior into a structured, quantifiable, and technically meaningful basic heat characterization quantity.

[0077] Step S2, in constructing the basic characterization of heat, includes the following steps:

[0078] Based on the statistical analysis of basic behavioral data with limitations on public opinion targets and time domains:

[0079] The public opinion object identification results output in step S1 As an object constraint, heat is used to calculate the time domain; As a time constraint, existing mature network behavior measurement and log statistics technologies are used to statistically analyze information behaviors directly related to the public opinion object in cyberspace, and the following basic statistical quantities are determined:

[0080] In the time domain Inside, and marked as The cumulative number of information releases related to the public opinion target, including the public opinion target identifier. The public opinion object identification result output in step S1 The symbolic representations of the two correspond one-to-one and are used for object constraints in subsequent statistical and computational processes;

[0081] The cumulative number of interactive behaviors generated in response to the published information within the same time domain;

[0082] The number of distinct users who participated in the aforementioned posting or interaction within the time domain, wherein users are counted to be unique based on their account identifiers.

[0083] Construct basic representations of popularity based on the scale of dissemination:

[0084] Based on the statistical results of information release, a basic representation quantity for characterizing the scale of public opinion dissemination is constructed, and its expression is as follows:

[0085]

[0086] in, This represents the scale of public opinion events explicitly published and mentioned within the time domain of popularity calculation, reflecting the degree of direct exposure of public opinion in cyberspace. This metric does not involve weight adjustments or normalization; its value comes directly from objective statistical results within the limited time domain.

[0087] Construct basic representations of interaction activity level as a type of popularity:

[0088] Based on the statistical results of interactive behavior, a basic representation quantity reflecting the level of public opinion dissemination activity is constructed. The interactive activity representation quantity is:

[0089] ;

[0090] Among them, molecules This indicates that public opinion-related information is in the time domain. The total amount of interactive behavior triggered within, denominator Indicates the corresponding information release scale. At least 1.

[0091] Constructing a basic representation of the breadth of participation in popularity:

[0092] By quantifying the scale of participating entities, a basic representation quantity reflecting the scope of public opinion influence is constructed, the expression of which is:

[0093] ;

[0094] in, This represents the number of different users who participated in the release or interaction of information related to the public opinion object within the time domain of the heat calculation, and is used to characterize the breadth of the spread of public opinion at the user level.

[0095] The final output of step S2 is a set of fundamental thermal characteristics, expressed as follows:

[0096] .

[0097] Unlike existing mature technologies that typically calculate public opinion heat directly based on a single behavioral indicator or empirical rule, this step does not generate a comprehensive heat value or early warning judgment at this stage. Instead, under the premise of clearly defining the public opinion object and time domain, it breaks down, quantifies, and structures the constituent elements of public opinion heat, so that each basic characterization quantity has a clear technical meaning and an independent statistical source, thereby laying the foundation for unified modeling and analysis of public opinion heat status in subsequent steps.

[0098] S3. Construct a heat state quantity using the basic heat characterization quantity to obtain the public opinion heat state result;

[0099] Step S3 is based on the set of fundamental thermal characterization quantities output in step S2. We construct a structured, computable, and transferable representation of public opinion heat status, which is used to transform discrete basic heat status representations into a unified heat status description form.

[0100] Step S3, in constructing the heat state quantity, includes the following steps:

[0101] Unified scaling of fundamental thermal characteristics:

[0102] The basic thermal characterization quantities output in step S2 , , By performing mapping processing at a unified scale, the representation quantities with different physical meanings can be combined.

[0103] Structured combination of multidimensional thermal characterization quantities:

[0104] Based on a unified scale, the mapped basic heat index representations are combined according to a preset structural relationship to form a multi-dimensional heat index state description structure, which is used to reflect the comprehensive state of public opinion in three dimensions: scale, activity level, and spread.

[0105] Stabilized output of thermal state results:

[0106] The combined heat status result is formalized so that it can be used as an independent and clear technical output, which can be directly called by subsequent steps without relying on implicit contextual information.

[0107] Step S3 ultimately outputs a public opinion heat state vector, the expression of which is:

[0108]

[0109] in:

[0110] :Depend on The scale thermal state component obtained after uniform scale mapping;

[0111] :Depend on The active heat state component obtained after uniform scale mapping;

[0112] :Depend on The propagation breadth and heat state components obtained after uniform scale mapping.

[0113] This heat state vector As a standardized representation of intermediate heat states, it provides a unified, comparable, and reusable input for subsequent steps.

[0114] Compared to existing mature public opinion heat processing technologies, step S3 differs primarily in the following ways: It no longer calculates heat values ​​directly based on single metrics such as readership or interaction volume. Instead, it strictly constructs the public opinion heat status based on the multi-dimensional heat base representations established in step S2, thus avoiding the simple mixing of indicators with different physical meanings. Furthermore, this step does not rely on empirical rules or thresholds to determine the level of public opinion, but instead outputs a heat status vector that can be used for subsequent evolutionary analysis, which is more conducive to characterizing the process of public opinion change. In addition, step S3 explicitly limits its input to only the output results of step S2, no longer directly using raw public opinion behavior data. This enhances the clarity of the technical hierarchy, the interpretability of the results, and the stability of the overall solution in terms of the methodological process structure.

[0115] S4. Use the public opinion heat status results to construct a description of heat change results, and obtain the public opinion heat change results;

[0116] Step S4 introduces a time dimension based on the obtained public opinion heat status results, and descriptively constructs the changes in public opinion heat status over time, thereby transforming the static heat status results formed in step S3 into heat change results that can reflect the development process of public opinion.

[0117] Step S4, in constructing the description of heat change results, includes the following steps:

[0118] Time series construction of public opinion heat status results:

[0119] According to the heat calculation time domain determined in step S1, the public opinion heat status results obtained in step S3 in different time domains are arranged, and existing mature time series construction methods are used to form a state sequence of public opinion heat status changing over time.

[0120] Description of thermal state changes in adjacent time domains:

[0121] After completing the construction of the time series of public opinion heat status, the existing mature time series difference analysis method is used to compare and analyze the results of public opinion heat status in adjacent time domains in order to characterize the changes of heat status in the time dimension.

[0122] Summary of heat variation characteristics across multiple time domains:

[0123] After obtaining descriptions of the heat changes in multiple adjacent time domains, existing mature trend induction and change feature processing techniques are further used to summarize the changes in the continuous time domain, forming descriptive results that can reflect the overall change characteristics of public opinion heat.

[0124] Step S4 ultimately outputs the result of public opinion heat change. This result describes the change of public opinion heat status within the time domain determined in step S1, and its expression is:

[0125] ;

[0126] in,

[0127] : This indicates the directional description of the change in public opinion intensity over time, used to characterize whether the intensity is rising, falling, or maintaining a certain direction of change in adjacent or continuous time domains;

[0128] : This describes the magnitude of changes in public opinion intensity, used to characterize the intensity of changes in public opinion intensity across different time domains;

[0129] This indicates the trend of public opinion intensity over a continuous time domain, reflecting the overall evolution of public opinion intensity.

[0130] S5. Use the results of changes in public opinion intensity to make early warning judgments and obtain public opinion intensity early warning results;

[0131] Step S5 will use the public opinion heat change results obtained in step S4. As the sole input, the degree of abnormality in changes in public opinion intensity is determined, and a public opinion intensity early warning result that can be directly applied is generated.

[0132] Step S5, when making a warning determination, includes the following steps:

[0133] Mapping of heat change indicators:

[0134] The descriptive results of step S4 Transformed into a set of identifiable abnormal indicators:

[0135] ;

[0136] in:

[0137] As an indicator of directional change, The direction of change is mapped to a numerical judgment result (e.g., rising / falling / stable corresponds to a fixed value).

[0138] As an indicator of amplitude change, The magnitude description is mapped to a numerical anomaly level;

[0139] As a trend change indicator, The continuous change characteristics are mapped to numerical determination.

[0140] Comprehensive anomaly assessment:

[0141] The various sub-indicators are combined according to a unified logic to form a comprehensive judgment value. :

[0142]

[0143] in:

[0144] This is a quantitative value for determining abnormal public opinion intensity.

[0145] , , To establish fixed weight parameters, historical public opinion data statistical analysis techniques and risk labeling backtracking mechanisms are used to set them. This setting process includes:

[0146] First, long-term statistics are conducted on different types of public opinion intensity sub-indicators (such as magnitude of change, rate of change, duration, or intensity of spread) within a historical public opinion sample set. These sub-indicators are then combined with actual early warning results or risk events to construct a correspondence between "public opinion indicators and risk outcomes." Subsequently, through statistical correlation analysis, threshold trigger frequency analysis, or historical hit rate evaluation, the relative importance of each sub-indicator in public opinion early warning judgment is determined. Based on this importance, corresponding settings are then established. , , The value of is determined by the magnitude of the value, which determines the different weights of influence in the overall judgment result.

[0147] Public opinion heat warning results generated:

[0148] Based on the comprehensive judgment value The final public opinion heat warning result is generated by comparing it with the preset trigger threshold:

[0149]

[0150] in:

[0151] This indicates that an abnormal level of public opinion has triggered an alert. This indicates that it has not been triggered.

[0152] The warning threshold is determined based on historical public opinion statistical analysis and risk labeling alignment technology. The determination process includes:

[0153] First, based on the historical public opinion dataset, the results of the public opinion heat change output in step S4 are analyzed. Long-term statistical modeling is conducted, and public opinion risk labels that have been manually confirmed or post-verified are introduced simultaneously to form a corresponding sample set of "public opinion heat change results - whether risk is triggered"; secondly, through distribution characteristic analysis and critical interval identification, the transition from "normal public opinion state" to "risk public opinion state" in historical samples is identified. The range of values ​​is concentrated; based on this, and combining the principle of balancing false trigger rate and false miss rate, the value that can be stably crossed in most historical risk samples and rarely touched in non-risk samples is selected as the warning threshold.

[0154] The threshold remains fixed during the method operation phase to ensure the certainty and interpretability of the warning judgment result. Its update is only completed during the offline analysis phase based on newly added historical samples and does not participate in the real-time calculation process, thereby ensuring the stable execution of the warning judgment logic in step S5.

[0155] The difference between this step and existing mature technologies is that the early warning judgment no longer directly relies on the original public opinion data or sentiment analysis indicators, but is based solely on the public opinion heat change results output in step S4; by independently analyzing and combining the three sub-indicators of directionality, magnitude and trend in the heat change to form a comprehensive judgment result, the early warning judgment process is ensured to have clear interpretability and reproducibility; at the same time, the early warning output results adopt a structured and closed form, which can be directly used as input for subsequent early warning modules, thereby achieving technical decoupling between the early warning judgment and public opinion analysis processes, forming an independent, complete and traceable technical chain from change results to early warning results.

[0156] Example 2:

[0157] This embodiment provides a system for calculating and issuing early warnings of online public opinion intensity. Based on a method for calculating and issuing early warnings of online public opinion intensity, it includes the following modules:

[0158] The module for identifying public opinion objects and determining the time domain corresponds to method step S1. This module is used to define the online public opinion to be analyzed at the object level in the early stage of system operation, and simultaneously determine the time domain range on which the public opinion heat calculation depends, so as to provide a unified analysis object and time benchmark for all subsequent heat calculations and analyses.

[0159] The module for calculating the basic characteristics of public opinion, corresponding to step S2, is used to quantify the basic components of public opinion popularity under the premise that the public opinion object and time domain have been clearly defined, so as to form a structured and reusable basic characteristics of public opinion.

[0160] The public opinion heat status construction module, corresponding to method step S3, is used to structurally integrate multiple basic heat indicators to construct a public opinion heat status result that can reflect the current heat level of public opinion as a whole, realizing the mapping from "multi-dimensional basic quantities" to "single state quantities".

[0161] The public opinion heat change analysis module corresponds to method step S4. This module is used to describe the changes in the public opinion heat status within adjacent or continuous time intervals, forming a quantitative characterization of the evolution trend of public opinion heat.

[0162] The public opinion heat warning and judgment module, corresponding to method step S5, is used to determine whether the public opinion heat has reached the warning conditions based on the results of public opinion heat changes, and output a clear warning result to achieve the final functional goal of the system.

[0163] Example 3:

[0164] This embodiment provides a scenario in which a system and method for calculating and warning about the popularity of online public opinion is applied.

[0165] In the e-government platform of a large city, a system for calculating and warning about the intensity of online public opinion has been deployed. This system continuously monitors and provides risk warnings for online public opinion related to public safety, urban operations, and people's livelihoods. The system connects to news websites, mainstream social media platforms, online forums, and Q&A communities, and performs real-time analysis of public opinion changes related to emergencies. The operation process includes:

[0166] Public opinion target identification and time domain determination:

[0167] When urban management departments take notice of a public event (such as public attention drawn by a traffic accident in the city), the system first receives a description of the event's theme and keywords.

[0168] Based on a pre-defined set of online information carriers, the system uses existing mature keyword retrieval and topic identification technologies to retrieve and aggregate relevant information in cyberspace, forming a candidate set of public opinion objects related to the public event.

[0169] Based on this, the system generates a unique public opinion object identifier for the public event and determines the time range for calculating public opinion heat, such as from the time when the event was first reported online to the current system running time, thereby establishing a unified public opinion object and time benchmark for subsequent analysis.

[0170] Calculation of basic thermal characterization quantities:

[0171] After the target of public opinion and the time domain are determined, the system organizes the relevant public opinion data within the time range.

[0172] The system collects basic information about the event, such as the number of posts, user comments and reposts, and the scale of users participating in the discussion, on different online platforms, and then processes this information in a structured manner.

[0173] Through this process, the system obtains a set of basic heat characterization results that can reflect the level of activity, scale of discussion and breadth of attention of the public event in cyberspace, providing basic data support for further analysis.

[0174] Building Public Opinion Trends:

[0175] After obtaining the basic characteristics of the popularity, the system processes the popularity information from different dimensions in a unified manner and integrates it into a popularity status result that can reflect the current popularity level of public opinion as a whole.

[0176] This heat status result is used to characterize the overall level of online attention to the public event within the current time window, enabling managers to intuitively understand the heat level of public opinion.

[0177] Analysis of changes in public opinion intensity:

[0178] As the system continues to run, it periodically acquires the public opinion heat status results at different time periods and compares and analyzes the heat status in adjacent time periods.

[0179] Using mature time series analysis methods, the system identifies whether the public opinion heat shows a significant increase, a sustained high level, or a gradual decline, forming descriptive results on the changes in public opinion heat to reflect the development trend of public opinion.

[0180] Public opinion heat warning judgment:

[0181] After obtaining the results of changes in public opinion intensity, the system compares them with pre-set public opinion early warning judgment rules. When the changes in public opinion intensity are detected to meet the early warning conditions, the system automatically generates the corresponding public opinion intensity early warning result.

[0182] For example, when public opinion heat rises rapidly in a short period of time and remains at a high level, the system can determine it as a high-level warning and push the warning result to the monitoring interface of the government management platform or the relevant responsible departments so that timely countermeasures can be taken.

[0183] Example 4:

[0184] This embodiment also provides a computer device applicable to a system and method for calculating and warning about the popularity of online public opinion, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the system and method for calculating and warning about the popularity of online public opinion as proposed in the above embodiment.

[0185] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a system and method for calculating and warning about online public opinion intensity as proposed in the above embodiments.

[0186] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0187] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0188] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0189] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0190] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0191] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for calculating and issuing early warnings of online public opinion intensity, characterized in that, Includes the following steps: S1. Determine the public opinion object and its corresponding heat calculation time domain; S2. Construct basic representations of popularity using public opinion objects and time domains to obtain a set of basic representations of popularity. S3. Construct a heat state quantity using the basic heat characterization quantity to obtain the public opinion heat state result; S4. Use the public opinion heat status results to construct a description of heat change results, and obtain the public opinion heat change results; S5. Use the results of changes in public opinion intensity to make early warning judgments and obtain public opinion intensity early warning results.

2. The method for calculating and early warning of online public opinion heat according to claim 1, characterized in that, Step S1, through the analysis and determination of the public opinion object and the analysis and determination of the heat calculation time domain, clarifies the unique public opinion object and the corresponding heat calculation time domain for subsequent public opinion heat calculation and early warning analysis, and obtains the following results: ,in: This indicates the results of the analysis and identification of public opinion targets; This indicates the time domain for calculating the popularity of the aforementioned public opinion object.

3. The method for calculating and early warning of online public opinion heat according to claim 1, characterized in that, Step S2, under the premise that step S1 has completed the unique identification of the public opinion object and the time domain limitation of its heat calculation, transforms the originally scattered and incomparable online public opinion behavior into a structured, quantifiable, and technically meaningful basic heat representation quantity. Step S2, in constructing the basic characterization of heat, includes the following steps: Based on the statistical analysis of basic behavioral data limited by the target of public opinion and the time domain; Construct basic characterization metrics for popularity based on dissemination scale; Construct basic representations of interactive activity levels for popularity; Construct basic characterization metrics for participation breadth-based popularity; The final output of step S2 is a set of fundamental thermal characteristics, expressed as follows: 。 4. The method for calculating and issuing early warnings of online public opinion heat according to claim 3, characterized in that, In step S2, when performing basic behavior volume statistics based on the public opinion object and time domain limitation, the public opinion object identification results output in step S1 are used. As an object constraint, heat is used to calculate the time domain; As a time constraint, network behavior measurement and log statistics techniques are used to statistically analyze information behaviors directly related to the public opinion object in cyberspace, and the following basic statistical quantities are determined: In the time domain Inside, and marked as The cumulative number of information releases related to the public opinion target, including the public opinion target identifier. The public opinion object identification result output in step S1 Symbolic representation; The cumulative number of interactive behaviors generated in response to the published information within the same time domain; The number of different users who participated in the aforementioned posting or interaction within the time domain.

5. The method for calculating and warning of online public opinion heat according to claim 3, characterized in that, In step S2, when constructing the basic representation quantity of the spread scale type of popularity, based on the statistical results of information release, a basic representation quantity for representing the spread scale of public opinion is constructed, the expression of which is: in, This indicates the scale of public opinion objects that are explicitly published and mentioned within the time domain of popularity calculation.

6. The method for calculating and early warning of online public opinion heat according to claim 3, characterized in that, In step S2, when constructing the basic representation of interactive activity level, a basic representation reflecting the level of public opinion dissemination activity is constructed based on the statistical results of interactive behavior. The interactive activity representation is as follows: ； Among them, molecules This indicates that public opinion-related information is in the time domain. The total amount of interactive behavior triggered within, denominator This indicates the scale of the information release.

7. The method for calculating and early warning of online public opinion heat according to claim 3, characterized in that, In step S2, when constructing the basic representation of the breadth of participation in public opinion, a basic representation reflecting the scope of public opinion influence is constructed by quantifying the scale of participating entities. Its expression is as follows: ； in, This indicates the number of different users who participated in the release or interaction of information related to the public opinion object within the time domain of the popularity calculation.

8. The method for calculating and early warning of online public opinion heat according to claim 1, characterized in that, Step S3 is based on the set of thermal fundamental characterization quantities output in step S2. Construct a structured, computable, and transferable representation of public opinion heat status to transform discrete basic heat status representations into a unified heat status description form; Step S3 ultimately outputs a public opinion heat state vector, the expression of which is: in: :Depend on The scale thermal state component obtained after uniform scale mapping; :Depend on The active heat state component obtained after uniform scale mapping; :Depend on The propagation breadth and heat state components obtained after uniform scale mapping.

9. The method for calculating and early warning of online public opinion heat according to claim 1, characterized in that, Step S4 introduces a time dimension based on the obtained public opinion heat status results, and descriptively constructs the changes in public opinion heat status over time, thereby transforming the static heat status results formed in step S3 into heat change results that can reflect the development process of public opinion. Step S5 determines the degree of abnormality of the change in public opinion heat based on the results of the change in public opinion heat obtained in step S4, and generates a public opinion heat warning result that can be directly applied.

10. A system for calculating and warning of online public opinion intensity, constructed based on the method for calculating and warning of online public opinion intensity as described in any one of claims 1-9, characterized in that, It includes the following modules: public opinion object identification and time domain determination module, basic heat index calculation module, public opinion heat index status construction module, public opinion heat index change analysis module, and public opinion heat index early warning judgment module.

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