News public opinion early warning system based on artificial intelligence

Abstract

The invention discloses a news public opinion early warning system based on artificial intelligence. The news public opinion early warning system comprises a data acquisition unit, a preprocessing unit, an analysis unit and an early warning unit, the data acquisition unit is used for capturing news contents of mainstream news websites, social media and forums in real time through a web crawler technology; the preprocessing unit is used for performing text cleaning, word segmentation, part-of-speech tagging and entity recognition on news contents; the analysis unit is used for performing sentiment analysis, theme classification and propagation trend prediction on the preprocessed news; and the early warning unit is used for calculating a public opinion risk index through risk assessment and carrying out early warning according to the public opinion risk index. Real-time monitoring and early warning of news public opinions are realized through an artificial intelligence technology, and the system has the advantages of high efficiency, accuracy and expansibility, and is suitable for multi-scene requirements of enterprises, governments and scientific research institutions.

Description

Technical Field

[0001] The present invention relates to the field of Internet information processing technology, and in particular to an online news and public opinion early warning service system based on artificial intelligence. Background Art

[0002] With the explosive growth of internet information, online news and public opinion are increasingly impacting businesses, governments, and society. Traditional public opinion monitoring methods rely on manual screening, which suffers from low efficiency, limited coverage, and poor real-time performance. While some existing systems incorporate automated tools, they lack the ability to deeply analyze public opinion sentiment, dissemination trends, and potential risks. Summary of the Invention

[0003] The present invention provides an artificial intelligence-based news and public opinion early warning system to solve the problems raised in the above background technology.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions:

[0005] An artificial intelligence-based news and public opinion early warning system includes a data collection unit, a preprocessing unit, an analysis unit, and an early warning unit;

[0006] Data collection unit: Use web crawler technology to capture news content from mainstream news websites, social media and forums in real time;

[0007] Preprocessing unit: performs text cleaning, word segmentation, part-of-speech tagging, and entity recognition on news content;

[0008] Analysis unit: performs sentiment analysis, topic classification, and communication trend prediction on pre-processed news;

[0009] Early warning unit: Calculate the public opinion risk index through risk assessment and issue early warnings based on the public opinion risk index.

[0010] As a further improvement of this technical solution: the news content is cleaned, specifically: HTML tags, special characters, and stop words are removed from the news content.

[0011] As a further improvement of this technical solution: perform word segmentation and part-of-speech tagging on the news content, specifically: input the news content into the GPT model for semantic analysis.

[0012] As a further improvement of this technical solution: perform entity recognition on news content, specifically: extract key entities of names of people, places, and organizations in the news.

[0013] As a further improvement of this technical solution: sentiment analysis is performed on the pre-processed news, specifically: the RoBERTa pre-trained model is used to determine the emotional tendency of the news, which includes positive, negative, and neutral.

[0014] As a further improvement of this technical solution: the pre-processed news is classified by subject, specifically: the news is classified by a deep learning classifier, and the classification categories include politics, economy, and society.

[0015] As a further improvement of this technical solution: predict the dissemination trend of the pre-processed news: predict the breadth of public opinion diffusion based on time series analysis.

[0016] As a further improvement of this technical solution: calculate the public opinion risk index through risk assessment, specifically:

[0017] Public opinion risk index R = (Q×W)×Y×U.

[0018] Among them, Q represents the sentiment score: positive is 1, negative is -1, and neutral is 0; W represents the sentiment score: the negative sentiment weight is 0.2, the neutral sentiment weight is 0.5, and the positive sentiment weight is 1.0; Y represents the dissemination breadth score: the single content reading volume ≥ 100,000 is 1.5, the forwarding chain level ≥ 3 is 1.8, and the number of dissemination platforms ≥ 5 is 1.2; U represents the news classification risk coefficient: the risk coefficient of political news is 1.5, the risk coefficient of science and technology news is 1.0, and the risk coefficient of social news is 1.2.

[0019] As a further improvement of this technical solution: the public opinion risk index is divided into levels:

[0020] Low risk: R < 1.0;

[0021] Medium risk: 1.0≤R<2.0;

[0022] High risk: 2.0≤R<3.0;

[0023] Extremely high risk: R ≥ 3.0.

[0024] As a further improvement of this technical solution: when the calculated public opinion risk index is greater than 1, an early warning notification will be triggered. After the early warning is triggered, a multi-channel warning will be issued through SMS, email, APP push and Web pop-up window.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] The present invention uses artificial intelligence technology to achieve real-time monitoring and early warning of news and public opinion, with the advantages of high efficiency, accuracy and scalability, and is suitable for the multi-scenario needs of enterprises, governments and scientific research institutions.

[0027] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and to implement it according to the contents of the description, the following preferred embodiments of the present invention are described in detail with reference to the accompanying drawings. The specific implementation methods of the present invention are given in detail by the following embodiments and the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0029] Figure 1 This is a structural diagram of an artificial intelligence-based news and public opinion early warning system proposed by the present invention. DETAILED DESCRIPTION

[0030] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples provided are intended only to illustrate the present invention and are not intended to limit the scope of the present invention. The following paragraphs describe the present invention in more detail by way of example with reference to the accompanying drawings. It should be noted that the drawings are all in a very simplified form and are not to exact scale, and are only used for the purpose of conveniently and clearly illustrating the embodiments of the present invention.

[0031] It should be noted that when a component is referred to as being "fixed to" another component, it may be directly on the other component or there may also be a central component. When a component is considered to be "connected to" another component, it may be directly connected to the other component or there may also be a central component. When a component is considered to be "set on" another component, it may be directly set on the other component or there may also be a central component. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0032] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains. The terms used in this specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0033] See also Figure 1 ,In an embodiment of the present invention, an artificial intelligence based news and public opinion early ,warning system includes a data collection unit, a pre-processing unit, an analysis unit, and an early ,warning unit;

[0034] Data collection unit: Use web crawler technology to capture news content from mainstream news websites, social media and forums in real time;

[0035] Preprocessing unit: This unit performs text cleaning, word segmentation and part-of-speech tagging, and entity recognition on news content. Specifically, it removes HTML tags, special characters, and stop words from the news content. The news content is then fed into the GPT model for semantic analysis, and finally, the key entities of names of people, places, and organizations in the news are extracted.

[0036] Analysis Unit: This unit conducts sentiment analysis, topic classification, and communication trend prediction on pre-processed news. Specifically, it uses the RoBERTa pre-trained model to determine the sentiment of news, which includes positive, negative, and neutral sentiment. It then uses a deep learning classifier to classify news (using the TextCNN model, which takes pre-processed text vectors as input and outputs category labels (e.g., politics, economy, society)). Finally, it predicts the spread of public opinion based on time series analysis.

[0037] Early warning unit: Calculates the public opinion risk index through risk assessment and issues early warnings based on the public opinion risk index, specifically:

[0038] Public opinion risk index R = (Q×W)×Y×U.

[0039] Among them, Q represents the sentiment score: positive is 1, negative is -1, and neutral is 0; W represents the sentiment score: the negative sentiment weight is 0.2, the neutral sentiment weight is 0.5, and the positive sentiment weight is 1.0; Y represents the dissemination breadth score: the single content reading volume ≥ 100,000 is 1.5, the forwarding chain level ≥ 3 is 1.8, and the number of dissemination platforms ≥ 5 is 1.2; U represents the news classification risk coefficient: the risk coefficient of political news is 1.5, the risk coefficient of science and technology news is 1.0, and the risk coefficient of social news is 1.2.

[0040] Public Opinion Risk Index Classification:

[0041] Low risk: R < 1.0;

[0042] Medium risk: 1.0≤R<2.0;

[0043] High risk: 2.0≤R<3.0;

[0044] Extremely high risk: R ≥ 3.0.

[0045] When the calculated public opinion risk index is greater than 1, an early warning notification will be triggered. After the warning is triggered, a multi-channel warning will be issued through SMS, email, APP push and web pop-up window.

[0046] The present invention also provides a visualization unit, which provides visualization interfaces such as public opinion heat maps, sentiment distribution maps, and communication path maps, and supports historical data backtracking and comparative analysis.

[0047] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any ordinary technician in this industry can smoothly implement the present invention as shown in the drawings and described above. However, any equivalent changes, modifications and evolutions made by technicians familiar with this profession without departing from the scope of the technical solution of the present invention using the technical content disclosed above are all equivalent embodiments of the present invention. At the same time, any equivalent changes, modifications and evolutions made to the above embodiments based on the essential technology of the present invention are still within the scope of protection of the technical solution of the present invention.

Claims

1. A news and public opinion early warning system based on artificial intelligence, characterized by: It includes data acquisition unit, pre-processing unit, analysis unit and early warning unit; Data collection unit: Use web crawler technology to capture news content from mainstream news websites, social media and forums in real time; Preprocessing unit: performs text cleaning, word segmentation, part-of-speech tagging, and entity recognition on news content; Analysis unit: performs sentiment analysis, topic classification, and communication trend prediction on pre-processed news; Early warning unit: Calculate the public opinion risk index through risk assessment and issue early warnings based on the public opinion risk index.

2. The artificial intelligence-based news and public opinion early warning system according to claim 1 is characterized in that: Perform text cleaning on news content, specifically removing HTML tags, special characters, and stop words from the news content.

3. The artificial intelligence-based news and public opinion early warning system according to claim 2 is characterized in that: Perform word segmentation and part-of-speech tagging on news content. Specifically, the news content is input into the GPT model for semantic analysis.

4. The artificial intelligence-based news and public opinion early warning system according to claim 3 is characterized in that: Perform entity recognition on news content, specifically: extract key entities such as names of people, places, and organizations in the news.

5. The artificial intelligence-based news and public opinion early warning system according to claim 4 is characterized in that: Sentiment analysis is performed on the pre-processed news. Specifically, the RoBERTa pre-trained model is used to determine the emotional tendency of the news, which includes positive, negative, and neutral.

6. The artificial intelligence-based news and public opinion early warning system according to claim 5 is characterized in that: The pre-processed news is classified by topic, specifically: news is classified by deep learning classifier, and the classification categories include politics, economy, and society.

7. The artificial intelligence-based news and public opinion early warning system according to claim 6 is characterized in that: Predict the dissemination trend of pre-processed news: predict the breadth of public opinion diffusion based on time series analysis.

8. The artificial intelligence-based news and public opinion early warning system according to claim 7 is characterized in that: The public opinion risk index is calculated through risk assessment, specifically: Public opinion risk index R = (Q×W)×Y×U. Among them, Q represents the sentiment score: positive is 1, negative is -1, and neutral is 0; W represents the sentiment score: the negative sentiment weight is 0.2, the neutral sentiment weight is 0.5, and the positive sentiment weight is 1.0; Y represents the dissemination breadth score: the single content reading volume ≥ 100,000 is 1.5, the forwarding chain level ≥ 3 is 1.8, and the number of dissemination platforms ≥ 5 is 1.2; U represents the news classification risk coefficient: the risk coefficient of political news is 1.5, the risk coefficient of science and technology news is 1.0, and the risk coefficient of social news is 1.

2.

9. The artificial intelligence-based news and public opinion early warning system according to claim 8, characterized in that: Public Opinion Risk Index Classification: Low risk: R < 1.0; Medium risk: 1.0≤R<2.0; High risk: 2.0≤R<3.0; Extremely high risk: R ≥ 3.

0.

10. The artificial intelligence-based news and public opinion early warning system according to claim 9, characterized in that: When the calculated public opinion risk index is greater than 1, an early warning notification will be triggered. After the warning is triggered, a multi-channel warning will be issued through SMS, email, APP push and web pop-up window.

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