Abnormality detection system, abnormality detection method, and program

The anomaly detection system effectively addresses the challenge of detecting anomalies across multiple SNS services by acquiring and analyzing post data, identifying relevant services, and performing sentiment analysis to accurately detect service-specific anomalies.

WO2025069315A1 Publication Date: 2025-04-03RAKUTEN GROUP INC
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Patent Information

Application Number
PCT/JP2023/035439
Authority / Receiving Office
WO · WO
Patent Type
Applications
Current Assignee / Owner
Filing Date
2023-09-28
Publication Date
2025-04-03

AI Technical Summary

Technical Problem

Existing anomaly detection systems are unable to effectively detect anomalies across multiple services in social networking services (SNS), as they are designed to monitor a single service and cannot analyze multiple posting data sets simultaneously.

Method used

The proposed anomaly detection system includes a post data acquisition unit, a post-related service specification unit, a sentiment analysis execution unit, and an abnormality detection unit. This system acquires post data from multiple SNS services, identifies the relevant service for each post, performs sentiment analysis, and detects anomalies based on the analysis results for each service.

Benefits of technology

The system enables proper detection of anomalies related to each of the multiple services by analyzing post data, improving the accuracy of anomaly detection and enabling timely intervention.

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Abstract

A post data acquisition unit (101) of an abnormality detection system (1) acquires each of a plurality of post data sets on a social networking service (SNS). From among a plurality of services to be subject to abnormality detection, a post-related services identification unit (102) identifies a post-related service that is related to each of the plurality of post data sets. An emotion analysis execution unit (103) executes emotion analysis on each of the plurality of post data sets. An abnormality detection unit (104) detects an abnormality related to each of the plurality of services on the basis of: the post-related service identified for each of the plurality of post data sets; and the result of the emotion analysis executed for each of the plurality of post data sets.
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