Enhancing Sentiment Analysis in Social Media Texts Using Transformer-Based NLP Models

Sentiment analysis plays a pivotal role in understanding public opinion and consumer sentiment expressed through social media platforms. Traditional methods of social media sentiment analysis texts are challenged by the use of informal languages, irony, and cultural variations. Here, they take into reason Transformer-based Natural Language Processing (NLP) models, notably BERT, to assess an enhancement in the stability and accuracy of the sentiment analysis. Using BERT-based models, Bi-LSTM, and dilated convolution, this research suggests novel methods for social media sentiment analysis. It successfully tackles the problems of informal language, maintaining extremely long-range context and lowering overfitting for sentiment classification in pre-trained BERT. The experiment demonstrates that BERT can detect files containing malicious code with an accuracy of 85%. When done on SMTs, there was a 3% improvement in the categorization of sentiment into positive, negative, and neutral categories. This is evident when comparing BERT to other well-known models, like Naive Bayes and Support Vector Machines, where the former performs better than the latter due to its enhanced capacity to identify sentiment expressions and contextual cues. The suggested results' generalization is connected to the real-world applications of brand analysis, public opinion research, and social media monitoring. BERT makes it feasible to examine consumer attitudes and market trends in greater detail than just scale points. Future research priorities include enhancing BERT's effectiveness in settings when computing resources are limited, offering resources for model interpretability, and shifting to using BERT for data that is multimodal and polyglot.