Contextual Embedding-Based Natural Language Processing Models for Sentiment Analysis and Opinion Mining

Abstract

The rapid expansion of social media platforms, online reviews, and digital communication systems has led to an exponential increase in user-generated textual data. Understanding public opinion from this data has become a critical task for businesses, governments, and research organizations. Sentiment analysis and opinion mining aim to extract subjective information such as polarity, emotions, and attitudes from natural language text. However, traditional bag-of-words and shallow machine learning models often fail to capture semantic context, word dependencies, and polysemy in natural language. This research proposes a contextual embedding-based natural language processing framework for sentiment analysis and opinion mining. The framework leverages transformer-based contextual embeddings such as BERT, ROBERT, and domain-adapted language models to capture deep semantic representations of text. These embeddings are further processed using deep neural classifiers to improve sentiment polarity detection and fine-grained opinion classification. The proposed approach integrates token-level contextual understanding, attention mechanisms, and domain-aware fine-tuning to enhance sentiment prediction accuracy. Experimental evaluation demonstrates that contextual embedding-based models significantly outperform traditional machine learning and static embedding approaches such as TF-IDF, Word2Vec, and GloVe in terms of accuracy, F1-score, and robustness across domains. The framework also shows improved performance in handling sarcasm, negation, and contextual ambiguity.