In the era of global e-commerce, understanding customer sentiment across diverse languages is vital for enhancing user experience and business intelligence. This project, titled "Multilingual Sentiment Analysis in E-commerce Platform", focuses on predicting customer sentiment—positive, negative, or neutral—based on product reviews submitted in multiple languages. The core objective is to bridge the language gap in online feedback interpretation using advanced machine learning and natural language processing techniques. To achieve this, a hybrid approach leveraging both deep learning and traditional models is implemented—specifically, BERT (Bidirectional Encoder Representations from Transformers) for robust text embeddings and contextual understanding, and Random Forest for efficient classification.

The dataset used for training and evaluation consists of key parameters such as Review ID, Language, Product Category, Review Text, and Sentiment. Multilingual review texts are preprocessed and encoded using BERT to capture semantic nuances across languages. These embeddings are then passed into the Random Forest classifier to determine the sentiment class. This approach ensures both contextual richness and high interpretability of the sentiment prediction process.

This system empowers e-commerce platforms to automatically analyze user feedback, regardless of the language used, offering real-time insights into customer satisfaction and product perception. The model’s multilingual capability significantly improves sentiment analysis accuracy across global markets. Ultimately, the project aims to support businesses in making informed decisions based on diverse customer sentiments, improving customer engagement and service personalization.

Keywords: Multilingual Sentiment Analysis, E-commerce, BERT, Random Forest, Natural Language Processing, Product Review Classification, Text Mining, Customer Feedback, Language Agnostic Model.