Decoding Customer Sentiments on Cosmetic Products Using AI

The rapid increase in the number of e-commerce platforms has resulted in a massive amount of customer feedback data, particularly in the cosmetic industry, where customer preferences and satisfaction levels are constantly fluctuating. The manual analysis of such large-scale data is inefficient and time-consuming, thereby requiring automated and intelligent analysis tools. This project proposes a machine learning-based solution to unravel customer sentiment and product performance in cosmetic products based on numerical review data. Unlike the traditional sentiment analysis technique, which relies on text-based customer reviews, the proposed system is based on structured numerical attributes such as product ratings, number of reviews, sentiment scores, popularity scores, and weighted product scores obtained by feature engineering. The data set undergoes rigorous preprocessing, including data cleaning, normalization, and outlier treatment, to ensure data integrity and authenticity. To identify hidden patterns and similarities among cosmetic products, K-Means clustering, an unsupervised machine learning algorithm, is used to cluster products based on customer feedback patterns. The clustering algorithm allows efficient product segmentation, which in turn helps to identify high-performing, medium-performing, and low-performing product segments. Visualization tools such as cluster plots and distribution plots are utilized to analyze the results of the clustering algorithm and facilitate analytical tasks. The experimental results show that the proposed approach is capable of identifying the trends of customer sentiment and the popularity of products without using text data. The proposed system can be used to improve the recommendation system of ecommerce websites and cosmetic companies to understand the preferences of customers. The proposed approach is also scalable to be used in other fields where the customer feedback is available in numerical form.