Sentimental Analysis of E-Commerce Customer Feedback using BERT model

E-commerce has emerged as the dominant sector within the public cloud market, mainly due to the growing trend of online shopping and video streaming services. Customer purchasing decisions are highly influenced by product reviews from previous buyers, wherein favorable feedback often enhances the visibility and ratings of products. Sentiment analysis is a rapidly evolving field that applies advanced text mining techniques to interpret and evaluate customer feedback. This paper presents a new approach to sentiment analysis in e-commerce using BERT, which is a state-of-the-art transformer model popular for its contextual understanding of language. The proposed methodology includes data cleaning and preprocessing followed by sentiment classification by BERT to analyze the text feedback and corresponding ratings. Highly accurately categorizing customer sentiments, the model applies deep contextual embeddings and attention mechanisms from BERT to derive richer insights from reviews. Work that uses BERT differently for the analysis of in-depth, scalable, and robust customer sentiment will help build the transformer-based techniques approach in e-commerce sentiment.