An Empirical Comparison of Claude, Llama, and Gemini for Aspect-Level Sentiment

Abstract

Aspect-based sentiment analysis provides granular insights into customer feedback by identifying discrete aspects, such as features or topics, and assigning a corresponding sentiment to each. This study assesses three large language models, hereafter referred to as LLMs, namely Google Gemini 2.5 Flash-Lite, Anthropic Claude Sonnet-4 delivered through AWS Bedrock, and Meta LLaMA 3.3 70B delivered through AWS Bedrock, using a real-world multilingual corpus of 7,841 Turkish mobile banking app reviews from İşbank in Turkey. We employ a prompt-based tagging protocol to extract aspect–sentiment pairs from every review, and we compare accuracy, F1-score, inference cost, and latency. The results show that all three LLMs can execute multilingual aspect extraction and sentiment categorization without task-specific fine-tuning. Claude Sonnet-4 attains the highest F1 for aspect extraction and the highest sentiment accuracy, although it incurs a markedly higher inference cost. Gemini 2.5 Flash-Lite achieves competitive accuracy at a fraction of the price, making it well-suited for high-volume analytics. Meta LLaMA at the 70B scale accessed through AWS Bedrock exhibits intermediate performance with moderate cost and latency. We provide detailed performance tables and figures, along with best-practice guidance for enterprise deployment. AWS Bedrock enables the strategic selection of Claude and LLaMA 3.3 70B for multilingual sentiment analysis, offering valuable insights from app reviews within scale, accuracy, and budget constraints.