FraudLens-Fraud App Detection Using Sentimental Analysis

FraudLens-Fraud App Detection Using Sentimental Analysis

Ravi Kumar Kalipu1, Deepika Mahanthi 2, Sumitra Yernagula 3, Raju Gudapuvalasa 4, Rohan Krishna Kanth Ommi 5, Asritha Raj Datla 6, Naresh Tripurana 7

1 Assistant Professor, 2 Student, 3 Student, 4 Student, 5 Student, 6 Student, 7 Student, 1234567Computer Science and Information Technology, Lendi Institute of Engineering and Technology, Vizianagaram, India

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Abstract - This project develops a Fraud App Detection system using user review analysis and Machine Learning, employing a Random Forest Classifier trained on labelled reviews categorized as fraudulent or safe. Text reviews are converted to numerical data using TF-IDF vectorization for efficient prediction. The user interface, built with Gradio, features a sleek black-themed design where users can upload app images and CSV files containing reviews for analysis. After processing, the system predicts whether the reviews indicate a fraudulent or safe app, displaying results through visual charts and providing downloadable predictions. By combining NLP and Machine Learning, this project offers a practical solution for identifying harmful apps based on user feedback, enhancing security, promoting user safety, and assisting app stores in maintaining a clean ecosystem.

Key Words: Fraud App Detection, Machine Learning, Random Forest Classifier, TF-IDF Vectorization, Natural Language Processing (NLP), User Review Analysis, Gradio Interface, App Security, Fraudulent App Identification, Safe App Classification.