AI-Driven Skincare Product Analytics & Review-Sentiment Modeling

Abstract

The rapid growth of online skincare markets has led to an overwhelming amount of customer reviews, making it difficult for consumers and companies to extract meaningful insights. This project presents an AI-driven skincare product analytics and sentiment analysis system that leverages Natural Language Processing (NLP) and Machine Learning techniques to analyze and classify customer feedback. The system processes textual data from skincare product reviews by performing data preprocessing steps such as text cleaning, tokenization, and stop-word removal. It then transforms the processed text into numerical features using the TF-IDF vectorization technique. A supervised machine learning model is trained on this data to classify sentiments into categories such as positive, negative, and neutral. To enhance usability, the trained model is integrated into a web-based application developed using Flask, allowing users to input reviews and receive real-time sentiment predictions. This system helps businesses understand customer opinions, identify product strengths and weaknesses, and make data-driven decisions.

Introduction

With the growth of the skincare industry and online platforms, large amounts of customer review data are generated, making manual analysis difficult. To address this, the project uses Artificial Intelligence (AI) and Natural Language Processing (NLP) to automatically analyze reviews and classify them as positive, negative, or neutral. This helps businesses understand customer opinions, satisfaction, and areas for improvement.

The proposed system follows a structured pipeline: data collection, text preprocessing (cleaning, tokenization, stop-word removal), feature extraction using TF-IDF, and sentiment classification using machine learning models like Logistic Regression and Naïve Bayes. The model is evaluated using metrics such as accuracy, precision, recall, and F1-score.

The system is deployed as a web application using Flask, allowing users to input reviews and receive real-time sentiment predictions. It also provides visual insights through dashboards for better understanding of trends and demand.

Results show that the model achieves good performance (around 88% accuracy) and effectively analyzes customer feedback. Overall, the system offers an efficient, scalable solution for extracting insights from reviews, supporting better decision-making, improving product quality, and enhancing customer experience.

Conclusion

This study presents an ai-driven system for analyzing skincare product reviews using natural language processing and machine learning techniques. The proposed approach effectively preprocesses textual data, extracts meaningful features using tf-idf, and classifies sentiments with high accuracy. The integration of demand prediction, opportunity scoring, and skill gap analysis provides deeper insights into customer feedback and product performance. Furthermore, the deployment of a web-based application enables real-time interaction and visualization of results. Overall, the system demonstrates a scalable and efficient solution for sentiment analysis and decision support, making it valuable for enhancing product quality and customer satisfaction in the skincare industry.

References

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Copyright

Copyright © 2026 Mr. A.. Libonce , Avula Gnanendra Reddy, Avula Likitha, Avulappagari Shirisha, G Sneha, Galeti Sree Lakshmi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.