Sentiment Analysis for Personalized Chatbot in E-commerce using Transformer BERT

This paper presents an innovative framework for a sentiment-aware, personalized online shopping chatbots, powered by the BERT architecture, at e-commerce businesses. Most current online shopping platform chatbots use simple predefined rules or shallow matching schemes to create responses and, as a result, they lack context and emotional awareness when responding to users. In response, the authors of this paper advocate for the use of a fine-tuned BERT Classifier that detects customer's true sentiment (positive, neutral or negative), in real-time. This real-time detection of sentiment greatly affects the generation of the chatbot's response as it assists the chatbot with personalized product recommendations, empathetic support and contextually appropriate conversation flow. The BERT Classifier was trained using an online retail product review database and was implemented through a Flask/Django backend for real-time interaction. The authors have found that the BERT Classifier has an overall accuracy of 93.1% and provides superior performance compared to standard machine learning classifiers (SVM, LSTM, etc.), demonstrating that transformer-based tools can generate more personalized, more satisfactory and more accurate answers to customer queries.