Sentiment Analysis Using Transfer Learning for E-Commerce Websites

Sentiment Analysis has emerged as a pivotal technique for understanding user opinions and sentiments expressed in textual data. In the context of e-commerce websites, where vast amounts of user-generated content are generated daily, leveraging the power of Transfer Learning becomes crucial for building effective sentiment analysis models. This research focuses on the application of Transfer Learning techniques in the domain of e-commerce sentiment analysis, utilizing pretrained transformer-based models. Aspect-based sentiment Analysis (ABSA) is a crucial natural language processing task that involves analyzing sentiment expressions toward specific aspects or features within a given text. This paper presents an approach to ABSA utilizing transformer-based deep learning models. Specifically, we leverage the Hugging Face Transformers library, which provides access to state-of-the-art pretrained models. Our method employs the BERT (Bidirectional Encoder Representations from Transformers) model, a powerful transformer architecture, for sentiment classification. This research contributes to the growing field of sentiment analysis by providing a robust and scalable approach to ABSA using transformer-based learning. The combination of transformer architectures and pretrained models demonstrates the capability to capture intricate sentiment nuances, making the proposed method a valuable asset for applications in customer feedback analysis, product reviews, and other domains where aspect-level sentiment understanding is crucial. The application of transfer learning in sentiment analysis for e-commerce not only contributes to advancing the field of natural language processing but also provides practical solutions for businesses seeking to harness the wealth of customer sentiment embedded in textual data on their online platforms.