Understanding human emotions: Advances in Sentiment Analysis

Abstract

Sentiment analysis”, a crucial part of “natural language processing (NLP)”, it includes considering people’s emotions and feelings on a subject from text to determine the writer's emotional tone. This paper tries to understand different   methods and models for sentiment analysis, including traditional approaches such as rule-based        systems and    machine           learning classifiers, as well as techniques for “deep learning” like “Recurrent Neural Networks (RNNs)”, “Long Short-Term Memory (LSTM)”, and “Transformers”. By            comparing       these techniques, we analyse their effectiveness across different datasets and languages. The study highlights key challenges such as sarcasm detection, context understanding, and handling demonstrate the effect of pre-trained language models, especially BERT and GPT, in improving sentiment classification accuracy. The paper concludes by discussing potential applications in analysing the feebacks given by customers, and ‘social media’ monitoring, and decision-making processes.