Leveraging Transfer Learning in LLMs for E-commerce Sentiment Analysis

EasyChair Preprint 14526

Abstract

With the rapid expansion of e-commerce, understanding customer sentiment has become crucial for businesses aiming to improve customer experience and drive sales. Sentiment analysis, a key component of natural language processing (NLP), allows businesses to gain insights from vast amounts of customer feedback. However, traditional sentiment analysis models often struggle with domain-specific language and context, limiting their effectiveness in specialized areas like e-commerce. This research explores the application of transfer learning in large language models (LLMs) to enhance sentiment analysis for e-commerce platforms.

Transfer learning in LLMs, such as GPT and BERT, allows models pre-trained on extensive and diverse datasets to be fine-tuned for specific tasks, such as e-commerce sentiment analysis. This approach leverages the broad linguistic knowledge embedded in these models while adapting them to understand the unique language patterns, product terminologies, and context-specific expressions found in customer reviews and feedback on e-commerce platforms.

In conclusion, this research demonstrates the significant potential of leveraging transfer learning in LLMs to enhance sentiment analysis in the e-commerce sector. By fine-tuning pre-trained LLMs for e-commerce-specific tasks, businesses can achieve more accurate sentiment analysis, leading to better customer insights and improved decision-making. The findings underscore the importance of continuing to refine and adapt LLMs for specialized applications, ensuring that businesses can fully harness the power of AI-driven sentiment analysis.

Keyphrases: Contextual Understanding, Domain Specific Language, LLMs, NLP, Sentiment Analysis, Transfer Learning, customer feedback, e-commerce, fine-tuning, large language models, real-time sentiment analysis

@booklet{EasyChair:14526,
  author    = {Dylan Stilinki},
  title     = {Leveraging Transfer Learning in LLMs for E-commerce Sentiment Analysis},
  howpublished = {EasyChair Preprint 14526},
  year      = {EasyChair, 2024}}