SENTIMENT ANALYSIS AND FAKE REVIEW DETECTION

In the era of digital commerce, customer reviews play a crucial role in shaping customer decisions and businessstrategies. They have an impact on price, target audience segmentation, marketing, product positioning, brandloyalty and purchase decisions. Through the utilization of sentiment analysis, e-commerce platforms can gleaninsights from the textual comments they receive from customers. This project presents a dual-purpose machinelearning approach for sentiment analysis and fake review detection in customer feedback data. It focuses onsentiment analysis of customer reviews and fake reviews detection using machine learning techniques to classifyreal reviews as positive, negative or neutral. We utilized a dataset of customer reviews. By leverging NaturalLanguage Processing (NLP) and machine learning algorithms such as Logistic Regression, Random Forest, KNearest Neighbors, Decision Tree, Gradient Boosting, XGBoost, CatBoost, Support Vector Machine, NaïveBayes, the system efficiently detects real reviews and extracts meaningful insights from textual data. Performancecomparison is made using barchart and confusion matrices. We evaluate their performance using various metricsincluding accuracy, precision, recall and F1 score and rank the models comparatively based on these results.. Theresults affirm that well-optimized traditional machine learning models can effectively detect fake content andextract sentiment without the need for complex deep learning frameworks. This approach holds strong potentialfor real-world deployment in e-commerce platforms, review-based recommendation engines, and feedbackmoderation systems.