NLP for Sentiment Analysis: A Review of Techniques and Real-World Applications
Abstract
Natural Language Processing (NLP) has been instrumental in understanding human emotions through sentiment analysis across social media, customer reviews, and financial markets. This paper explores traditional approaches like lexicon-based methods and machine learning classifiers, as well as deep learning-based sentiment analysis using Transformer models such as BERT and RoBERTa. We discuss challenges related to multilingual sentiment analysis, sarcasm detection, and domain-specific adaptability, highlighting future research directions to enhance sentiment classification accuracy.
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