Binary Sentiment Analysis and Sentiment Marketing Strategy Optimization of E-Commerce Platform User Comments Based on Deep Learning Algorithm

Abstract

Binary sentiment analysis plays a crucial role in e-commerce marketing strategy optimization by classifying user comments into positive or negative sentiments, allowing businesses to analyze customer feedback, predict user behavior, and enhance personalized marketing efforts. This paper explores the application of the bi-gram Hidden Markov Optimization (bi-gram HMMO) model in sentiment analysis within e-commerce platforms in China. Leveraging natural language processing techniques, the bi-gram HMMO model captures intricate dependencies between consecutive words to discern user sentiment from textual data such as reviews and comments. Through a systematic analysis of user interactions, the model accurately identifies and categorizes emotional states, providing valuable insights into customer satisfaction levels and areas for improvement. BERT + JOA achieved a 95.6% accuracy and a 95.4% F1-score, outperforming traditional models like Bi-gram HMM (78.5%) and CNN + JOA (91.5%). Sentiment-aware marketing strategies lead to higher customer engagement, conversion rates, and revenue growth. After implementing sentiment-optimized strategies, customer conversion increased by 73.3% (from 4.5% to 7.8%), while customer retention improved by 24.9% (from 55.2% to 68.9%). Click-through rates (CTR) doubled (+103.1%), and sentiment-based product recommendations saw a 136.6% increase in engagement, indicating that customers respond better to personalized and emotionally intelligent marketing campaigns. Additionally, monthly revenue growth surged by 127.0%, while the average order value (AOV) increased by 33.4% (from $52.3 to $69.8), demonstrating that sentiment-aware marketing strategies directly influence e-commerce profitability. The findings reveal a prevalence of positive sentiment in user expressions, particularly towards product quality, delivery speed, and customer service, with probability scores ranging from 0.75 to 0.90. Conversely, instances of negative sentiment are associated with product defects or damaged items, yielding lower probability scores of 0.60 to 0.80.