Multilingual Sentiment Analysis For E-Commerce Platform

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.

@article{ijter2025212507240876,
  author = {Bojja Manisha Ratnam and Prof. Ch Satyananda Reddy},
  title = {Multilingual Sentiment Analysis For E-Commerce Platform},
  journal = {International Journal of Technology &  Emerging Research },
  year = {2025},
  volume = {1},
  number = {3},
  pages = {174-179},
  issn = {3068-109X},
  url = {https://www.ijter.org/article/212507240876/multilingual-sentiment-analysis-for-e-commerce-platform},
  abstract = {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.},
  keywords = {Multilingual Sentiment Analysis, E-commerce, BERT, Random Forest, Natural Language Processing, Product Review Classification, Customer Feedback},
  month = {Jul},
}

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