Aspect-Based Sentiment Analysis for Customer Reviews Using Contextual Representation Learning
Keywords:
aspect-based sentiment analysis; contextual representation learning; customer reviews; transformer models; governance; deployment; fairness; sustainability; policyAbstract
Aspect-based sentiment analysis has become a foundational capability for organizations that seek to derive actionable insight from large volumes of customer reviews. Unlike document-level or sentence-level sentiment classification, aspect-based analysis requires systems to jointly identify opinion targets, attribute sentiment polarities to those targets, and manage complex linguistic phenomena such as negation, sarcasm, and implicit opinion expression. Contextual representation learning has reshaped this task by providing dynamic word representations that capture syntactic and semantic dependencies across review texts. However, deploying such models in production environments introduces substantial system-level challenges beyond predictive accuracy. This paper presents a systems-oriented examination of aspect-based sentiment analysis using contextual representation learning. It discusses architectural trade-offs in encoder design, task decomposition, and fine-tuning strategies. It further analyzes data governance, annotation infrastructure, model deployment, runtime observability, robustness, fairness, sustainability, and policy implications. The discussion emphasizes that technical performance cannot be separated from broader organizational and societal concerns. Effective production systems require careful integration of pretrained language models with reliable data pipelines, monitoring mechanisms, fairness audits, and governance frameworks. The paper provides a critical synthesis of existing approaches and identifies forward-looking directions for building responsible, maintainable, and contextually aware sentiment analysis infrastructures.
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