Sentiment Analysis Based on RoBERTa for Amazon Review: An Empirical Study on Decision Making

TL;DR

This study uses transformer-based models to perform sentiment analysis on Amazon product reviews and demonstrates the efficacy of advanced NLP models in sentiment analysis and offers valuable insights into consumer behavior, with implications for strategic decision-making and marketing practices.

Abstract

In this study, we leverage state-of-the-art Natural Language Processing (NLP) techniques to perform sentiment analysis on Amazon product reviews. By employing transformer-based models, RoBERTa, we analyze a vast dataset to derive sentiment scores that accurately reflect the emotional tones of the reviews. We provide an in-depth explanation of the underlying principles of these models and evaluate their performance in generating sentiment scores. Further, we conduct comprehensive data analysis and visualization to identify patterns and trends in sentiment scores, examining their alignment with behavioral economics principles such as electronic word of mouth (eWOM), consumer emotional reactions, and the confirmation bias. Our findings demonstrate the efficacy of advanced NLP models in sentiment analysis and offer valuable insights into consumer behavior, with implications for strategic decision-making and marketing practices.

Figures (15)

• Figure 1: History of NLP
• Figure 2: Transformer Model Architecture
• Figure 3: Scaled Dot-Product Attention
• Figure 4: Multi-Head Attention
• Figure 5: Overall pre-training and fine-tuning procedures for BERT.