Paper Contents
Abstract
Sentiment analysis is an essential method for understanding and evaluating the enormous amounts of text data generated across multiple platforms. Rapid advancements in Natural Language Processing (NLP), particularly the introduction of pre-trained language models such as BERT, RoBERTa, and hybrid architectures, sentiment analysis has become much more extensive and accurate. Applications of these advanced models in sentiment analysis, highlighting their ability to improve classification accuracy and capture contextual nuances. Combining different modelsResNeXt for feature extraction, BiLSTM with self-attention for context representation, and RoBERTa for language interpretationdevelops a robust framework for sentiment analysis. Comparative studies across multiple datasets demonstrate that hybrid models perform better at handling complex phrase patterns and a variety of expressions of sentiment. The power of pre-trained models in sentiment analysis is a Turning point, unlocking new possibilities for understanding the emotions and opinions of people on social media and in customer feedback. Additional issues including sarcasm detection, domain-specific adaptations, and context generation are tackled, Paving the way for future innovations in this field.
Copyright
Copyright © 2024 M PRAVEEN. This is an open access article distributed under the Creative Commons Attribution License.
