Abstract

Opinion extraction from large-scale text has become an essential task in natural language processing (NLP) and computational linguistics due to the rapid proliferation of user-generated content on social media, reviews, and forums. This paper explores computational linguistic models designed to identify, extract, and analyze opinions from massive textual datasets. Techniques such as machine learning, deep learning, lexicon-based approaches, and hybrid models are evaluated for their efficiency, scalability, and accuracy in detecting subjective information, sentiment polarity, and opinion targets. The study emphasizes the challenges posed by language ambiguity, sarcasm, context dependency, and domain specificity in large-scale opinion extraction tasks. Additionally, the integration of pre-trained language models, such as BERT, GPT-based embeddings, and transformer architectures, is highlighted as a transformative approach that significantly improves performance over traditional methods. The paper also examines evaluation metrics, including precision, recall, F1-score, and domain-specific adaptation techniques. Finally, the advantages and disadvantages of each model type are discussed, providing a comparative framework for researchers and practitioners seeking effective computational solutions for large-scale opinion mining. The findings underscore the potential of advanced computational linguistic models in driving data-driven decision-making and enhancing human-computer interaction