{"repo":"AmirhosseinHonardoust/Fake-Review-Detector","free":true,"listed":false,"github":"https://github.com/AmirhosseinHonardoust/Fake-Review-Detector","clone":"git clone https://github.com/AmirhosseinHonardoust/Fake-Review-Detector.git","description":"An AI-powered Fake Review Detector built with Python, Streamlit, and Scikit-learn. Uses TF-IDF vectorization, Logistic Regression, and behavioral text analytics (sentiment, exclamations, clichés) to identify synthetic or spammy product reviews. Includes training scripts and a full interactive dashboard.","language":"Python","stars":28,"topics":["ai-project","dashboard","data-science","data-visualization","fake-review-detection","logistic-regression","machine-learning","natural-language-processing","nlp","python"],"license":"MIT","category":"machine-learning","readme_excerpt":"Fake Review Detector (NLP + Streamlit) A machine learning project that detects fake vs real product reviews using TF-IDF vectorization , Logistic Regression , and behavioral text features such as exclamation count, sentiment, and repeated promotional phrases. It also includes a sleek Streamlit app for interactive real-time predictions. --- Features - Text cleaning and normalization pipeline - Hybrid feature extraction: - TF-IDF (1–2 grams) - Numeric sentiment & behavioral features - Interpretable Logistic Regression model - Evaluation metrics: Confusion Matrix, ROC, and PR curves - Interactive Streamlit app with adjustable decision threshold --- Folder Structure --- How It Works 1. Data Input: CSV containing text and label columns. 2. Preprocessing: URL, punctuation, and HTML removal + lowercasing. 3. Feature Engineering: - Sentiment score - Exclamation & ALL-CAPS detection - Fake-review clichés (e.g., “best product ever”) 4. Modeling: Logistic Regression trained on combined features. 5. Prediction: Threshold-tunable classification for FAKE vs REAL. --- Streamlit Interface Below is a preview of the web app UI built with Streamlit: --- Highlights - Paste or type any review text. - Adjust decision threshold for sensitivity. - Get immediate prediction with fake probability. - Built-in tips to help interpret the model. --- Model Evaluation Confusion Matrix --- Precision-Recall Curve --- ROC Curve The model achieves AUC ≈ 1.00 and AP ≈ 1.00 on sample data (balanced, synthetic). --","default_branch":null,"files":null,"tree":[],"storefront":"/r/AmirhosseinHonardoust","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/AmirhosseinHonardoust/Fake-Review-Detector/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}