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Fraud App Detection using Sentimental Analysis.

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Název: Fraud App Detection using Sentimental Analysis.
Autoři: Rahangdale, Ganesh, Craig, Joel, Gopikrishnan, Poossan, Butle, Shivam, Sheikh, Shoyeb, Narnavere, Priya
Zdroj: Journal of Applied Information Science; 2023, Vol. 11 Issue 2, p29-34, 6p
Témata: USER-generated content, FRAUD investigation, SENTIMENT analysis, WEB-based user interfaces, FRAUD, ACQUISITION of data
Geografický termín: JAVA (Indonesia)
Abstrakt: This project aims to develop a fraud app detection system using sentiment analysis. The system leverages Java 1.8 Spring Boot, React, HTML, CSS, JavaScript, and Bootstrap to create a robust web application. The methodology involves collecting user reviews and comments, preprocessing the data, and applying sentiment analysis models to determine sentiment scores. The system then uses predefined fraud criteria to flag potentially fraudulent reviews. Integrating the system into a Java Spring Boot backend and visualizing results using React provides real-time monitoring and investigation. Continuous improvement, user feedback handling, and effective model selection ensure enhanced accuracy and adaptability to evolving fraudulent patterns. This project presents an integrated fraud detection system for user reviews, utilizing sentiment analysis within a Java 1.8 Spring Boot backend and React frontend. It encompasses data collection, preprocessing, sentiment analysis, and predefined fraud criteria to flag suspicious reviews. Real-time monitoring and investigation capabilities are offered through an intuitive web interface. The project's commitment to continuous improvement, user feedback integration, and effective model selection ensures adaptability to evolving fraudulent patterns, enhancing accuracy and preserving the credibility of online platforms in an era where user-generated content profoundly influences consumer decisions. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Applied Information Science is the property of Publishing India Group and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Databáze: Complementary Index
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DbLabel: Complementary Index
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  Data: Fraud App Detection using Sentimental Analysis.
– Name: Author
  Label: Authors
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  Data: <searchLink fieldCode="AR" term="%22Rahangdale%2C+Ganesh%22">Rahangdale, Ganesh</searchLink><br /><searchLink fieldCode="AR" term="%22Craig%2C+Joel%22">Craig, Joel</searchLink><br /><searchLink fieldCode="AR" term="%22Gopikrishnan%2C+Poossan%22">Gopikrishnan, Poossan</searchLink><br /><searchLink fieldCode="AR" term="%22Butle%2C+Shivam%22">Butle, Shivam</searchLink><br /><searchLink fieldCode="AR" term="%22Sheikh%2C+Shoyeb%22">Sheikh, Shoyeb</searchLink><br /><searchLink fieldCode="AR" term="%22Narnavere%2C+Priya%22">Narnavere, Priya</searchLink>
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  Data: Journal of Applied Information Science; 2023, Vol. 11 Issue 2, p29-34, 6p
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22USER-generated+content%22">USER-generated content</searchLink><br /><searchLink fieldCode="DE" term="%22FRAUD+investigation%22">FRAUD investigation</searchLink><br /><searchLink fieldCode="DE" term="%22SENTIMENT+analysis%22">SENTIMENT analysis</searchLink><br /><searchLink fieldCode="DE" term="%22WEB-based+user+interfaces%22">WEB-based user interfaces</searchLink><br /><searchLink fieldCode="DE" term="%22FRAUD%22">FRAUD</searchLink><br /><searchLink fieldCode="DE" term="%22ACQUISITION+of+data%22">ACQUISITION of data</searchLink>
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  Label: Geographic Terms
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  Data: <searchLink fieldCode="DE" term="%22JAVA+%28Indonesia%29%22">JAVA (Indonesia)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This project aims to develop a fraud app detection system using sentiment analysis. The system leverages Java 1.8 Spring Boot, React, HTML, CSS, JavaScript, and Bootstrap to create a robust web application. The methodology involves collecting user reviews and comments, preprocessing the data, and applying sentiment analysis models to determine sentiment scores. The system then uses predefined fraud criteria to flag potentially fraudulent reviews. Integrating the system into a Java Spring Boot backend and visualizing results using React provides real-time monitoring and investigation. Continuous improvement, user feedback handling, and effective model selection ensure enhanced accuracy and adaptability to evolving fraudulent patterns. This project presents an integrated fraud detection system for user reviews, utilizing sentiment analysis within a Java 1.8 Spring Boot backend and React frontend. It encompasses data collection, preprocessing, sentiment analysis, and predefined fraud criteria to flag suspicious reviews. Real-time monitoring and investigation capabilities are offered through an intuitive web interface. The project's commitment to continuous improvement, user feedback integration, and effective model selection ensures adaptability to evolving fraudulent patterns, enhancing accuracy and preserving the credibility of online platforms in an era where user-generated content profoundly influences consumer decisions. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Applied Information Science is the property of Publishing India Group and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Code: eng
        Text: English
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        PageCount: 6
        StartPage: 29
    Subjects:
      – SubjectFull: JAVA (Indonesia)
        Type: general
      – SubjectFull: USER-generated content
        Type: general
      – SubjectFull: FRAUD investigation
        Type: general
      – SubjectFull: SENTIMENT analysis
        Type: general
      – SubjectFull: WEB-based user interfaces
        Type: general
      – SubjectFull: FRAUD
        Type: general
      – SubjectFull: ACQUISITION of data
        Type: general
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      – TitleFull: Fraud App Detection using Sentimental Analysis.
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          Name:
            NameFull: Rahangdale, Ganesh
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            NameFull: Craig, Joel
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            NameFull: Gopikrishnan, Poossan
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            NameFull: Butle, Shivam
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            NameFull: Sheikh, Shoyeb
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            – D: 01
              M: 07
              Text: 2023
              Type: published
              Y: 2023
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